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The probability of finding a spherical "hole" of a given radius r contains crucial structural information about many-body systems. Such hole statistics, including the void conditional nearest-neighbor probability functions GV(r), have been well studied for hard-sphere fluids in d-dimensional Euclidean space R d . However, little is known about these functions for hard-sphere crystals for values of r beyond the hard-sphere diameter, as large holes are extremely rare in crystal phases. To overcome these computational challenges, we introduce a biased-sampling scheme that accurately determines hole statistics for equilibrium hard spheres on ranges of r that far extend those that could be previously explored. We discover that GV(r) in crystal and hexatic states exhibits oscillations whose amplitudes increase rapidly with the packing fraction, which stands in contrast to GV(r) that monotonically increases with r for fluid states. The oscillations in GV(r) for 2D crystals are strongly correlated with the local orientational order metric in the vicinity of the holes, and variations in GV(r) for 3D states indicate a transition between tetrahedral and octahedral holes, demonstrating the power of GV(r) as a probe of local coordination geometry. To further study the statistics of interparticle spacing in hard-sphere systems, we compute the local packing fraction distribution f(ϕl) of Delaunay cells and find that, for d ≤ 3, the excess kurtosis of f(ϕl) switches sign at a certain transitional global packing fraction. Our accurate methods to access hole statistics in hard-sphere crystals at the challenging intermediate length scales reported here can be applied to understand the important problem of solvation and hydrophobicity in water at such length scales. [ABSTRACT FROM AUTHOR]

This article proposes a Bayesian optimization-based generalized regression neural network (BO-GRNN) framework for underwater source localization in shallow water environment. Specially, by leveraging kernel regression analysis, BO-GRNN fits a conditional probability density function using training and test samples to infer the source location of unseen test samples. Furthermore, BO-GRNN models the objective function as a Gaussian process (GP) based on observed samples, utilizing an acquisition function to sample around the best objective function values and high variance regions. By evaluating these samples and updating the GP iteratively, the optimal hyperparameters are identified, ultimately improving the training efficiency and localization performance of the GRNN. Simulation experiments and error analysis validate this framework's effectiveness. Further validation using SWellEx-96 experimental data demonstrates the superior localization performance of this framework, even with limited data snapshots. This framework estimates the range and depth of underwater sources within a 10 km range, achieving mean absolute errors of less than 0.15 km for range and 0.2 m for depth. Compared to GRNN, this framework demonstrates a 5 times improvement in training efficiency. These advantages make BO-GRNN an efficient, accurate, and robust solution for underwater source localization. [ABSTRACT FROM AUTHOR]

Educating students to apply probability literacy and reasoning in out-of-school contexts requires the previous preparation of teachers, which should include the assessment of their mathematical and didactic knowledge. Consequently, we investigated three research questions concerning the probabilistic reasoning and literacy of 66 prospective secondary school teachers when solving probability questions related to a report taken from the media news, their ability to identify the fundamental stochastic ideas needed to solve the task, and their capacity to predict their students' potential difficulties. The participants needed to compute the probability of a complementary event, several conditional probabilities, and perform critical reading of the information in the report. Despite the participants' good mathematical knowledge, a high percentage showed reasoning biases, such as the fallacy of the transposed conditional, confusion between conditioning and causation, and few identified the information missing to solve a question. There was a poor capacity to determine the stochastic ideas needed to solve the questions, beyond probability, sample space, conditional probability, and sampling. The participants quoted a few potential errors of their students, mainly conceptual mistakes with scarce recognition of interpretation errors. These results reveal the need to reinforce teachers' probabilistic literacy, reasoning, and related didactic knowledge. [ABSTRACT FROM AUTHOR]

Ultra‐high voltage (UHV) converter stations are critical nodes in power grids. This paper proposes a probabilistic framework for assessing and mitigating the seismic risk of UHV converter station systems to enhance the seismic performance of the grid. First, a Bayesian network model for the system functionality of UHV converter stations was established based on the enumeration of equipment failure scenarios. Conditional probability tables (CPTs) were used to represent the causal relationship among subsystems and system functionality. Inference calculations were conducted using Bayes' theorem. Then, the definition of system seismic loss risk distribution was proposed to assess the seismic risk of the system over its entire lifespan. The feasibility of this framework was validated using a specific UHV converter station, yielding analytical solutions for the probability distribution of system functionality and seismic vulnerability curves. Additionally, the cost‐effectiveness of several risk mitigation strategies was assessed. A cost‐benefit analysis was performed from the perspectives of both the expected loss of a single earthquake and the life‐cycle cost. The framework comprehensively considered the constraints imposed by series, parallel, and bypass control devices on the system's functionality. It was revealed that the seismic loss risk for UHV converter stations exhibits a characteristic of low probability but high loss. [ABSTRACT FROM AUTHOR]

Background: Medical students and professionals often struggle to understand medical test results, which can lead to poor medical decisions. Natural frequency tree-based training (NF-TT) has been suggested to help people correctly estimate the predictive value of medical tests. We aimed to compare the effectiveness of NF-TT with conventional conditional probability formula-based training (CP-FT) and investigate student variables that may influence NF-TT's effectiveness. Methods: We conducted a parallel group randomized controlled trial of NF-TT vs. CP-FT in two medical schools in South Korea (a 1:1 allocation ratio). Participants were randomly assigned to watch either NF-TT or CP-FT video at individual computer stations. NF-TT video showed how to translate relevant probabilistic information into natural frequencies using a tree structure to estimate the predictive values of screening tests. CP-FT video showed how to plug the same information into a mathematical formula to calculate predictive values. Both videos were 15 min long. The primary outcome was the accuracy in estimating the predictive value of screening tests assessed using multiple-choice questions at baseline, post-intervention (i.e., immediately after training), and one-month follow-up. The secondary outcome was the accuracy of conditional probabilistic reasoning in non-medical contexts, also assessed using multiple-choice questions, but only at follow-up as a measure of transfer of learning. 231 medical students completed their participation. Results: Overall, NF-TT was not more effective than CP-FT in improving the predictive value estimation accuracy at post-intervention (NF-TT: 87.13%, CP-FT: 86.03%, p =.86) and follow-up (NF-TT: 72.39%, CP-FT: 68.10%, p =.40) and facilitating transfer of training (NF-TT: 75.54%, CP-FT: 71.43%, p =.41). However, for participants without relevant prior training, NF-TT was more effective than CP-FT in improving estimation accuracy at follow-up (NF-TT: 74.86%, CP-FT: 58.71%, p =.02) and facilitating transfer of learning (NF-TT: 82.86%, CP-FT: 66.13%, p =.04). Conclusions: Introducing NF-TT early in the medical school curriculum, before students are exposed to a pervasive conditional probability formula-based approach, would offer the greatest benefit. Trial registration: Korea Disease Control and Prevention Agency Clinical Research Information Service KCT0004246 (the date of first trial registration: 27/08/2019). The full trial protocol can be accessed at https://cris.nih.go.kr/cris/search/detailSearch.do?seq=15616&search_page=L. [ABSTRACT FROM AUTHOR]

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an "emulator" as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case. [ABSTRACT FROM AUTHOR]

This paper scrutinizes the conventionalization of the Spanish expression la de  que (‘the amount of  that’), a reduced variant of la cantidad de  que. The study seeks to determine the diachrony of and mechanisms underlying the emergence and diffusion of the la de  que expression and whether it has conventionalized to develop into an independent form-function pairing. A Bayesian mixed-effects logistic regression analysis of approximately 2000 observations of diachronic corpus data tests the influence of the conditional probability of lexemes in the noun slot and the register, which both turn out to have a meaningful effect. It is argued that the initial omission of cantidad can be accounted for by appealing to the notion of probabilistic reduction, whereby omission is feasible in contexts involving a high degree of constructional predictability. In the mapping out of change, conventionalization of the innovative la de  que is most observable in contexts involving high constructional predictability and is least prominent in contexts of low constructional predictability. On the grounds that, over time, the la de  que progressively has become stylistically divergent from the longer expression, the two constructions are claimed to be functionally distinct. [ABSTRACT FROM AUTHOR]

This article evaluates the predictions of an algorithmic‐level distributed associative memory model as it introduces, propagates, and resolves ambiguity, and compares it to the predictions of computational‐level parallel parsing models in which ambiguous analyses are accounted separately in discrete distributions. By superposing activation patterns that serve as cues to other activation patterns, the model is able to maintain multiple syntactically complex analyses superposed in a finite working memory, propagate this ambiguity through multiple intervening words, then resolve this ambiguity in a way that produces a measurable predictor that is proportional to the log conditional probability of the disambiguating word given its context, marginalizing over all remaining analyses. The results are indeed consistent in cases of complex structural ambiguity with computational‐level parallel parsing models producing this same probability as a predictor, which have been shown reliably to predict human reading times. [ABSTRACT FROM AUTHOR]

Under the joint effects of climate change and human activities, the assumption of the stationarity of hydrological series has been overturned, which is of great significance in the field of hydrology. However, the current research on drought propagation is generally based on the assumption of sequence stationarity, in which related results may be biased. To this end, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model was used to construct a time-varying drought index. The seasonal propagation characteristics from meteorological to hydrological drought were examined based on conditional probability. The dynamic change of propagation characteristics was explored by utilizing the moving window, and the driving factors were revealed by using the variable importance in projection. The Luanhe River basin, which has a fragile ecological environment, was selected as a case study. The results indicated that 1) using a time-varying drought index was more reasonable than using a stationary assumption, and the latter relatively easily underestimated the propagation time, especially in summer; 2) drought propagation in summer and autumn was faster than that in spring and winter, and the propagation time showed a significant downward trend; 3) the trigger threshold (absolute value) of meteorological drought in spring was significantly higher than that in other seasons, and hydrological drought was more likely to be triggered by meteorological drought in autumn and winter; and 4) the precipitation P, decreasing runoff R, and increasing evaporation E were the main factors affecting the seasonal propagation characteristics. The findings of this study are of great significance for water resource management and further understanding of drought propagation mechanisms. [ABSTRACT FROM AUTHOR]

The bidirectional relationships between metabolic syndrome (MetS) and major depressive disorder (MDD) were discovered, but the influencing factors of the comorbidity were barely investigated. We aimed to fully explore the factors and their associations with MetS in MDD patients. The data were retrieved from the electronic medical records of a tertiary psychiatric hospital in Beijing from 2016 to 2021. The influencing factors were firstly explored by univariate analysis and multivariate logistic regressions. The propensity score matching was used to reduce the selection bias of participants. Then, the Bayesian networks (BNs) with hill-climbing algorithm and maximum likelihood estimation were preformed to explore the relationships between influencing factors with MetS in MDD patients. Totally, 4126 eligible subjects were included in the data analysis. The proportion rate of MetS was 32.6 % (95 % CI: 31.2 %–34.1 %). The multivariate logistic regression suggested that recurrent depression, uric acid, duration of depression, marriage, education, number of hospitalizations were significantly associated with MetS. In the BNs, number of hospitalizations and uric acid were directly connected with MetS. Recurrent depression and family history psychiatric diseases were indirectly connected with MetS. The conditional probability of MetS in MDD patients with family history of psychiatric diseases, recurrent depression and two or more times of hospitalizations was 37.6 %. Using the BNs, we found that number of hospitalizations, recurrent depression and family history of psychiatric diseases contributed to the probability of MetS, which could help to make health strategies for specific MDD patients. • The proportion rate of metabolism syndrome (MetS) was 32.6% in major depressive disorder (MDD) patients. • The number of hospitalizations and uric acid were directly connected with MetS. • Recurrent depression and family history psychiatric diseases were indirectly connected with MetS. • The conditional probability of MetS in MDD patients with family history, recurrent and hospitalization times ≥2 was 37.6%. [ABSTRACT FROM AUTHOR]

This work introduces the English Sublexical Toolkit, a suite of tools that utilizes an experience-dependent learning framework of sublexical knowledge to extract regularities from the English lexicon. The Toolkit quantifies the empirical regularity of sublexical units in both the reading and spelling directions (i.e., grapheme-to-phoneme and phoneme-to-grapheme) and at multiple grain sizes (i.e., phoneme/grapheme and onset/rime unit size). It can extract multiple experience-dependent regularity indices for words or pseudowords, including both frequency indices (e.g., grapheme frequency) and conditional probability indices (e.g., grapheme-to-phoneme probability). These tools provide (1) superior estimates of the regularities that better reflect the complexity of the sublexical system relative to previously published indices and (2) completely novel indices of sublexical units such as phonographeme frequency (i.e., combined units of individual phonemes and graphemes that are independent of processing direction). We demonstrate that measures from the toolkit explain significant amounts of variance in empirical data (naming of real words and lexical decision), and either outperform or are comparable to the best available consistency measures. The flexibility of the toolkit is further demonstrated by its ability to readily index the probability of different pseudowords pronunciations, and we report that the measures account for the majority of variance in these empirically observed probabilities. Overall, this work provides a framework and resources that can be flexibly used to identify optimal corpus-based consistency measures that help explain reading/spelling behaviors for real and pseudowords. [ABSTRACT FROM AUTHOR]

There is a lack of a quantitative assessment of the risk factors associated with poisoning and asphyxiation accidents in steel enterprises, especially the insufficient treatment of uncertainty in risk analysis. To address this issue, this work proposed a risk assessment method based on fuzzy Bayesian network (FBN), which established a risk assessment indicator system for poisoning and asphyxiation from four aspects, including human, material, environmental, and management factors, and illustrated the relationship between these risk factors through fault tree analysis (FTA). Taking a steel plant in China as an example, fuzzy set theory (FST) and expert surveys were combined to determine the prior probabilities and conditional probabilities of Bayesian network (BN) nodes. The results show that (i) the probability of poisoning and asphyxiation accidents in this steel plant is 74%; (ii) among the various influencing factors, defective or inadequate monitoring and alarm devices, isolation devices, equipment inspection systems, and toxic gas operation management are identified as the critical contributors; and (iii) this accident probability has decreased to 47% after rectification measures and reassessment. The findings of this research offer valuable insights for steel enterprises in preventing poisoning and asphyxiation accidents. [ABSTRACT FROM AUTHOR]

The use of different stances can provide tennis players with a tactical advantage since it enables them to cover a larger court area faster. This is especially critical since the entire stroke process takes only 1.5 seconds. However, it is unclear which stance is most suitable on the court. The purpose of the study was to predict the probability of the four stances used for forehand and two-handed backhand (2BH) in different court situations. Four influencing variables (landing zone of the ball (LZB), positioning of the player (PP), returning direction of the ball, landing zone of the returning ball) and one target variable (groundstroke stance) were collected from 3,850 successful shots at the Australian Open by a notation system to train a Bayesian network. Conditional probabilities of stance were estimated based on the two dominant influencing variables derived from Bayesian modelling. Both PP (0.53) and LZB (0.29) were identified as the most dominant influencing variables for stance selection. Probability distributions indicated that open and semi-open stances were most commonly used for forehand strokes, while closed stance was prevalent for 2BH strokes. Our preliminary findings provide insights into the court usage characteristics of the forehand and 2BH in dominant stances. [ABSTRACT FROM AUTHOR]

Purpose: Current observation period post-liver biopsy is typically 4 h. This study investigates the safety of reducing the observation period after percutaneous liver biopsy. Methods: Patients who underwent percutaneous liver biopsy between 2017 and 2022 in the Radiology Department of a tertiary centre were included in this retrospective, institutional review board-approved study. Patient demographics, procedure details and complication data were collected from the electronic medical records. Complications were graded according to the Cardiovascular and Interventional Radiological Society of Europe (CIRSE) classification. Conditional survival probabilities were calculated for the 4-h observation period. Results: Among 1125 patients, 275 complications were seen; 255 grade 1, 15 grade 2 and five grade 3. Post-procedural pain represented 93% (256) of complications, whereas post-procedural haemorrhage occurred in 17 (6%) patients: 13 were of grade 2 severity requiring prolonged observation, and 4 were of grade 3 severity. Of these grade 3 complications, two required blood transfusion whereas two required embolization. A total of 215 (78%) complications occurred within 1 h, 244 (89%) within 2 h of observation. 16 (94%) of 17 post-procedural haemorrhages occurred within 2 h post-biopsy. If complication-free after 2 h, the probability of experiencing a complication within the next 2 h was 4%. Conclusion: The majority of complications were identified within 2 h of observation. Complications recognised after this period were largely pain-related, with only one grade 3 complication seen (post-procedural haemorrhage).Our findings suggest 2 h of post-procedural observation may be safe. Level of Evidence: Level 2B, Retrospective Cohort Study. [ABSTRACT FROM AUTHOR]

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Commutation failure (CF) is one of the most prevalent events in line-commutated converter-based high-voltage direct current (LCC–HVDC) systems. The frequent occurrence of CF poses a significant threat to the safe and stable operation of power grids. The commutation failure prevention control (CFPREV) is the main method to prevent the initial CF, which relies on the detection of a drop in AC voltage. However, its slow fault detection hinders the rapid response of post-fault control, thereby affecting the effectiveness of CF suppression. Therefore, this paper proposes a fast fault detection method based on Bayesian theory. This algorithm can calculate the conditional probability of each variable in a given dataset, effectively mitigating the impact of noise and errors in data to yield precise and dependable results. By processing the collected continuous data and calculating the probability of the existence of a fault point, it determines whether a fault occurs. Based on this method, an improved prevention strategy is proposed, which can effectively enhance the CF resilience of LCC–HVDC systems under AC faults. Finally, using the power systems computer-aided design (PSCAD) platform, the accuracy of the fault probability detection algorithm was verified based on actual engineering data. The effectiveness of the proposed strategy was further validated under three typical fault scenarios, leading to significant improvements: a 64.12% reduction in detection time for three-phase grounding faults, a 69.88% decrease for single line-to-ground faults, and a 36.84% improvement in phase-to-phase fault detection. Additionally, the overall performance of the strategy was thoroughly assessed through extensive simulations covering various fault cases within a selected range of typical faults. The simulations demonstrated the superiority of the proposed strategy in CF mitigation, with a significant reduction in incidents from 89 to 34 out of 150 tested scenarios. This highlights the robustness and reliability of the proposed strategy. [ABSTRACT FROM AUTHOR]

Summary The present study aims at identifying sleep patterns in insomnia in a clinical sample using three strategies to define poor nights. Sleep diaries and self‐reported questionnaires were collected from 77 clinical patients with insomnia. The conditional probabilities of observing a poor night after 1, 2, or 3 consecutive poor nights were computed according to three strategies with same criteria for sleep onset latency, wake after sleep onset, and sleep efficiency, but varying criterion for total sleep time. Latent profile analyses were conducted to derive sleep patterns. Uni‐ and multivariate analyses were conducted to characterise the sleep patterns identified. A total of 1586 nights were analysed. The strategy used significantly influenced the average percentage of reported poor nights. Two to three sleep patterns were derived per strategy. Within each strategy, sleep patterns differed from each other on sleep variables and night‐to‐night variability. Results suggest the existence of sleep patterns in insomnia among individuals consulting in psychological clinics. Adding a total sleep time of 6‐h cut‐off as a criterion to define poor nights increases the accuracy of the strategy to define poor night and allows to identify sleep patterns of poor nights in insomnia. [ABSTRACT FROM AUTHOR]

This study presents a mechanistic framework for assessing and mitigating hydrogen leakage risks in Mobile Hydrogen Refueling Stations (MHRS) based on inherent safety concepts. A bow-tie model is proposed to identify potential hazards, event pathways, and consequences. Subsequently, this model is mapped into Bayesian Networks (BN) to enable quantitative risk assessment. Fuzzy set theory is adopted to address the uncertainty of prior probabilities, using a Noisy-MAX model to optimize the BN conditional probabilities. To mitigate leakage risks, a dedicated inherently safer design checklist is developed to identify opportunities for incorporating inherent safety. A case study demonstrates that the leakage potentials decrease from 0.2600 to 0.1997, and the leakage consequences decrease by approximately 22%. This work presents a novel mechanism using inherent safety to reduce the hydrogen leakage risk of MHRS, which is useful for creating an inherently safer MHRS with fundamental and natural robustness against hydrogen leakage. • Hydrogen leakage risk reduction in Mobile Hydrogen Refueling Stations (MHRS). • Bayesian networks based leakage risk assessment of MHRS. • Risk-based inherent safety strategies for hydrogen leakage prevention. • A mechanistic framework for MHRS leakage risk management based on inherent safety. [ABSTRACT FROM AUTHOR]

Traditional two-arm randomized trial designs have played a pivotal role in establishing the efficacy of medical interventions. However, their efficiency is often compromised when confronted with multiple experimental treatments or limited resources. In response to these challenges, the multi-arm multi-stage designs have emerged, enabling the simultaneous evaluation of multiple treatments within a single trial. In such an approach, if an arm meets efficacy success criteria at an interim stage, the whole trial stops and the arm is selected for further study. However when multiple treatment arms are active, stopping the trial at the moment one arm achieves success diminishes the probability of selecting the best arm. To address this issue, we have developed a group sequential multi-arm multi-stage survival trial design with an arm-specific stopping rule. The proposed method controls the familywise type I error in a strong sense and selects the best promising treatment arm with a high probability. [ABSTRACT FROM AUTHOR]

This note sheds new light on the exponentiation approximation of the probability that all K individuals have distinct birthdays across N calendar days. The exponentiation approximation imposes a pairwise independence assumption, which does not hold in general. We sidestep this assumption by deriving the conditional probability for each pair of individuals to have distinct birthdays given that previous pairs do. An interesting implication is that the conditional probability decreases in a step-function form—not in a strictly monotonical form—as more pairs are restricted to have distinct birthdays. The source of the step-function structure is identified and illustrated. We also establish the equivalence between the pairwise approach and another common approach based on permutations of all individuals. [ABSTRACT FROM AUTHOR]

Background: Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. Results: Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. Conclusions: Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach. [ABSTRACT FROM AUTHOR]

Suppose that in an M / G / 1 waiting queue, the server can choose between two service time distributions. We look for the choice that enables us to minimize the expected value of a cost criterion that takes into account the cost incurred by working faster and the time needed to empty the waiting line. The random final time is the first time there is no customer waiting for service. When the service times are exponential random variables, we can appeal to dynamic programming to obtain the optimal solution. In the general case, conditional probability is used. Particular problems in which the capacity of the system is finite are solved explicitly. [ABSTRACT FROM AUTHOR]

Many physical processes feature random telegraph signals, e.g., a time signal c (t) that randomly switches between two values over time. The present study focuses on the class of telegraphic processes for which the transition rates are formulated by using fractal-like expressions. By considering various restrictive hypotheses regarding the statistics of the waiting times, the present analysis provides the corresponding expressions of the unconditional and conditional probabilities, the mean waiting times, the mean phase duration, the autocorrelation function and the associated integral time scale, the spectral density, and the mean switching frequency. To assess the relevance of the various hypotheses, synthetically generated signals were constructed and used as references to evaluate the predictive quality of the theoretically derived expressions. The best predictions were obtained by considering that the waiting times probability density functions were Dirac peaks centered on the corresponding mean values. [ABSTRACT FROM AUTHOR]

The prediction of European heatwaves at the subseasonal range is of key importance to mitigate their impact. This study builds on previous work which identifies five main European heatwave types based on their atmospheric circulation patterns (CPs). These CPs are potential predictors of heatwaves, as these patterns are connected with a high probability of 2-meter temperature exceeding the 90th percentile. Therefore, the aim of this study is to use these patterns to construct a pattern-based forecast method. The skill of this method to forecast extreme warm temperatures is then assessed and compared with the direct grid-point based forecast (using the direct 2-meter temperature forecast of the model). The extended (or subseasonal) range reforecast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for the skill evaluation. Firstly, the skill of the extended range model is assessed in predicting CPs. The pattern-based methodology is then compared with the direct prediction of extreme warm temperatures. The results show that the pattern-based methodology has a low skill at the short to medium range compared to the direct method, however it maintains skill for longer lead times, extending the forecast skill horizon significantly by up to six days over key heatwave regions. This improvement is localized over regions with the highest conditional probability of extreme warm temperatures. Furthermore, the prediction skill of 4-day periods of high temperatures using CPs lasting at least five days is also assessed. A similar improvement in forecast skill horizon is observed but the improvement is more modest and even more localized. This methodology provides skilful forecast at longer lead times to the end of the medium range and into the subseasonal range, which would be beneficial for early warnings of European heatwaves and therefore support the timely implementation of mitigation plans. [ABSTRACT FROM AUTHOR]

Existing NL2SQL approaches do not fully utilize the information of data table columns, which plays an important role in the semantic understanding of the problem and the logical generation of SQL statements. This paper proposed an NL2SQL method that fused data table columns (FC-SQL) to improve the overall accuracy of SQL generation. Firstly, this method utilized BERT to merge the problem and database table columns for encoded representations. Secondly, it used multitask learning approach to construct a multi-task network by combining parallel and cascade to predict different sub-tasks. Finally, for the conditional value extraction sub-task, this method computed the similarity between the words in the problem and the table columns by fusing the information of the columns, and it used the similarity value as a weight to compute each word as the conditional value probability of each word as a conditional value, thus improving the accuracy of conditional value prediction. The logical form accuracy and SQL execution accuracy on the TableQA dataset reach 88.23% and 91.65%, respectively. This paper designed ablation experiments to verify the effect of table columns information on the model. The experimental results show that the incorporation of table columns improves the effectiveness of the conditional value extraction sub-task, which in turn improves the overall accuracy of the NL2SQL task and provides better SQL generation compared to the comparison model. [ABSTRACT FROM AUTHOR]

PCCP is the much discussed claim that the probability of a conditional A → B is conditional probability. Triviality results purport to show that PCCP -- as a general claim -- is false. A particularly interesting proof has been presented in (Hájek, 2011), who shows that -- even if a probability distribution P initially satisfied PCCP -- a rational update can produce a non-PCCP probability distribution. We argue that the notion of rational update in this argumentation is construed in much too broad a way. In order to make the argumentation precise, we discuss the general rules for modeling conditionals in probability spaces and present formalized version(s) of PCCP and of minimal assumptions concerning the appropriate spaces. Using the introduced apparatus we give a detailed analysis of Hájek's (2011) triviality proof and show that it is based on an application of revision rules which allow one to construct probability distributions violating not only PCCP, but also fundamental properties of conditionals. This means that they do not really provide arguments against PCCP, properly formalized. We also discuss a Dutch Book argument which shows that the updated belief system is not coherent. This gives an additional, strong argument against accepting the update rules. We also discuss the Converse Dutch Book theorem and argue, that even if the produced probability measure seems to violate it, it cannot serve as the counterexample, as it is not an appropriate model for conditionals. Ultimately, we show that important arguments against PCCP fail. [ABSTRACT FROM AUTHOR]

This study considers multiperiod bankruptcy prediction models, an aspect scarcely considered in research despite its importance, since creditors must assess the risk of loans over the entire life of the debt and not at a specific point in the future. Two possibilities for the implementation of multiperiod prediction models are considered: Multi-Model multiperiod Bankruptcy Prediction Models (MMBPM) and Single-Model multiperiod Bankruptcy Prediction Models (SMBPM). The former considers the conditional probabilities obtained by individual models predicting bankruptcy at specific times in the future, while the latter is a single model predicting bankruptcy at a specific time interval in the future. The results show that there are no significant differences between the two approaches when compared using data after the learning period. However, SMBPMs have the important advantage of interpretability for decision-making, which is discussed with examples. Moreover, a comparison of SMBPM performance with external references is performed. [ABSTRACT FROM AUTHOR]

This article investigates the mechanisms that underlie the biases in price elasticities of demand in applying static demand models under dynamic demand, which have been pointed out by previous empirical studies. It studies three sources of biases: disregard of state variables (affecting short-run elasticity); inconsistent utility parameter estimates; and changing expectations of consumers (affecting long-run elasticity). Disregard of state variables, such as durable product holdings, which is not negligible but not paid much attention to in the literature, leads to an overestimate of short-run own elasticities. Inconsistent utility parameter estimates arises due to the failure to account for consumers' future expectations and unobserved state variables. Changing expectations of consumers are not explicitly specified in the static model, and this also leads to biased results when applying static models. Regarding the magnitude of the biases, the first and the third sources of biases might induce large biases in price elasticities, especially when the focus is on the large conditional choice probability products. Possible remedies for the use of static demand models are also discussed. [ABSTRACT FROM AUTHOR]

Understanding and predicting interactions between predators and prey and their environment are fundamental for understanding food web structure, dynamics, and ecosystem function in both terrestrial and marine ecosystems. Thus, estimating the conditional associations between species and their environments is important for exploring connections or cooperative links in the ecosystem, which in turn can help to clarify such directional relationships. For this purpose, a relevant and practical statistical method is required to link presence/absence observations with biomass, abundance, and physical quantities obtained as continuous real values. These data are sometimes sparse in oceanic space and too short as time series data. To meet this challenge, we provide an approach based on applying categorical data analysis to present/absent observations and real‐number data. The real‐number data used as explanatory variables for the present/absent response variable are discretized based on the optimal detection of thresholds without any prior biological/ecological information. These discretized data express two different levels, such as large/small or high/low, which give experts a simple interpretation for investigating complicated associations in marine ecosystems. This approach is implemented in the previous statistical method called CATDAP developed by Sakamoto and Akaike in 1979. Our proposed approach consists of a two‐step procedure for categorical data analysis: (1) finding the appropriate threshold to discretize the real‐number data for applying an independent test; and (2) identifying the best conditional probability model to investigate the possible associations among the data based on a statistical information criterion. We perform a simulation study to validate our proposed approach and investigate whether the method's observation includes many zeros (zero‐inflated data), which can often occur in practical situations. Furthermore, the approach is applied to two datasets: (1) one collected during an international synoptic krill survey in the Scotia Sea west of the Antarctic Peninsula to investigate associations among krill, fin whale (Balaenoptera physalus), surface temperature, depth, slope in depth (flatter or steeper terrain), and temperature gradient (slope in temperature); (2) the other collected by ecosystem surveys conducted during August–September in 2014–2017 to investigate associations among common minke whales, the predatory fish Atlantic cod, and their main prey groups (zooplankton, 0‐group fish) in Arctic Ocean waters to the west and north of Svalbard, Norway. The R code summarizing our proposed numerical procedure is presented in S4S1. [ABSTRACT FROM AUTHOR]

This paper studies implications of the consistency conditions among prior, posteriors, and information sets on introspective properties of qualitative belief induced from information sets. The main result reformulates the consistency conditions as: (i) the information sets, without any assumption, almost surely form a partition; and (ii) the posterior at a state is equal to the Bayes conditional probability given the corresponding information set. The main implication of this result is to provide a tractable epistemic model which dispenses with the technical assumptions inherent in the standard epistemic model such as the countable number of information sets. Applications are agreement theorem, no-trade theorem, and the epistemic characterization of correlated equilibria. Implications are as follows. First, since qualitative belief reduces to fully introspective knowledge in the standard environment, a care must be taken when one studies non-veridical belief or non-introspective knowledge. Second, an information partition compatible with the consistency conditions is uniquely determined by the posteriors. Third, qualitative and probability-one beliefs satisfy truth axiom almost surely. The paper also sheds light on how the additivity of the posteriors yields negative introspective properties of beliefs. [ABSTRACT FROM AUTHOR]

In this paper, we perform estimates of vulnerability to multidimensional poverty for 17 Latin American countries at three points of time: 2005/2006, 2012, and 2017. We use a Multidimensional Bayesian Network Classifier model to estimate the conditional probability of being multidimensionally poor and then we use these probabilities and the standard downside semi‐deviation as the risk parameter to identify the vulnerable households. Despite significant reductions over the study period, in 2017 approximately 150 million people—excluding Guatemala, Nicaragua and Venezuela for which we do not have recent data—remained vulnerable to multidimensional poverty. We also observe that vulnerability to poverty is reduced at a much slower rate than poverty itself, revealing that achievements in SDG1 can be quite fragile. We perform a decomposition and find that as poverty decreases, risk‐induced vulnerability becomes relatively more important than poverty‐induced vulnerability. However, the poor‐vulnerable still constitute the core vulnerability group. [ABSTRACT FROM AUTHOR]

Fault localization (FL) is the key activity while debugging a program. Any improvement to this activity leads to significant improvement in total software development cost. In the paper, we present a conditional probability statistics based fault localization technique that derives the association between statement coverage information and test case execution result. This association with the failed test case result shows the fault containing probability of that specific statement. Subsequently, we use a grouping method to refine the obtained statement ranking sequence for better fault localization. We named our proposed FL technique as CGFL, it is an abbreviation of Conditional probability and Grouping based Fault Localization. We evaluated the effectiveness of the proposed method over eleven open-source data sets from Defects4j and SIR repositories. Our obtained results show that on average, the proposed CGFL method is 24.56% more effective than contemporary FL techniques namely D ∗ , Tarantula, Ochiai, Crosstab, BPNN, RBFNN, DNN, and CNN. [ABSTRACT FROM AUTHOR]

The main novelty of this paper consists of presenting a statistical artificial neural network (ANN)-based model for a robust prediction of the frequency-dependent aeroacoustic liner impedance using an aeroacoustic computational model (ACM) dataset of small size. The model, focusing on percentage of open area (POA) and sound pressure level (SPL) at a zero Mach number, takes into account uncertainties using a probabilistic formulation. The main difficulty in training an ANN-based model is the small size of the ACM dataset. The probabilistic learning carried out using the probabilistic learning on manifolds (PLoM) algorithm addresses this difficulty as it allows constructing a very large training dataset from learning the probabilistic model from a small dataset. A prior conditional probability model is presented for the PCA-based statistical reduced representation of the frequency-sampled vector of the log-resistance and reactance. It induces some statistical constraints that are not straightforwardly taken into account when training such an ANN-based model by classical optimizations methods under constraints. A second novelty of this paper consists of presenting an alternate solution that involves using conditional statistics estimated with learned realizations from PLoM. A numerical example is presented. [ABSTRACT FROM AUTHOR]

Abstract Background Medical students and professionals often struggle to understand medical test results, which can lead to poor medical decisions. Natural frequency tree-based training (NF-TT) has been suggested to help people correctly estimate the predictive value of medical tests. We aimed to compare the effectiveness of NF-TT with conventional conditional probability formula-based training (CP-FT) and investigate student variables that may influence NF-TT’s effectiveness. Methods We conducted a parallel group randomized controlled trial of NF-TT vs. CP-FT in two medical schools in South Korea (a 1:1 allocation ratio). Participants were randomly assigned to watch either NF-TT or CP-FT video at individual computer stations. NF-TT video showed how to translate relevant probabilistic information into natural frequencies using a tree structure to estimate the predictive values of screening tests. CP-FT video showed how to plug the same information into a mathematical formula to calculate predictive values. Both videos were 15 min long. The primary outcome was the accuracy in estimating the predictive value of screening tests assessed using multiple-choice questions at baseline, post-intervention (i.e., immediately after training), and one-month follow-up. The secondary outcome was the accuracy of conditional probabilistic reasoning in non-medical contexts, also assessed using multiple-choice questions, but only at follow-up as a measure of transfer of learning. 231 medical students completed their participation. Results Overall, NF-TT was not more effective than CP-FT in improving the predictive value estimation accuracy at post-intervention (NF-TT: 87.13%, CP-FT: 86.03%, p = .86) and follow-up (NF-TT: 72.39%, CP-FT: 68.10%, p = .40) and facilitating transfer of training (NF-TT: 75.54%, CP-FT: 71.43%, p = .41). However, for participants without relevant prior training, NF-TT was more effective than CP-FT in improving estimation accuracy at follow-up (NF-TT: 74.86%, CP-FT: 58.71%, p = .02) and facilitating transfer of learning (NF-TT: 82.86%, CP-FT: 66.13%, p = .04). Conclusions Introducing NF-TT early in the medical school curriculum, before students are exposed to a pervasive conditional probability formula-based approach, would offer the greatest benefit. Trial registration Korea Disease Control and Prevention Agency Clinical Research Information Service KCT0004246 (the date of first trial registration: 27/08/2019). The full trial protocol can be accessed at https://cris.nih.go.kr/cris/search/detailSearch.do?seq=15616&search_page=L .

In the present article, we propose a Jackknife empirical likelihood (JEL) ratio test for testing the independence of time to failure and cause of failure in competing risks data. We use the U-statistics theory to derive the JEL ratio test. The asymptotic distribution of the test statistic is shown to be the standard chi-square distribution. A Monte Carlo simulation study is carried out to assess the finite sample behavior of the proposed test. The performance of the proposed JEL test is compared with the test given by Dewan et al. (2004). Finally, we illustrate our test procedure using two real data sets.

The key to achieving an effective and sustainable geopark waste management system is fostering community participation. This research about the Gunung Sewu Geopark, Yogyakarta, Indonesia, examines the phases and levels of community participation in waste reduction. The study involved surveying 1,757 respondents aged 18 and above using door-to-door questionnaires, which were analysed using the conditional probability method to determine participation stages and levels. The results indicate that community participation in waste reduction activities, such as composting and recycling is inadequate, with only 21.12% of respondents participating in the decision-making stage, 1.99% participating in the implementation stage, and 0.68% finding waste reduction activities valuable. Rural community participation level scores were 0.68 and categorised as very low. The incomplete stages of participation and low levels of participation are significant challenges for implementing sustainable waste management and maintaining conservation principles in Gunung Sewu Geopark. Further research could be conducted to determine the driving factors that most influence rural community participation so that geopark policymakers can develop strategies to increase participation more effectively and to targets.

The relationship between metabolically healthy obesity (MHO) and cardiovascular disease (CVD) risk remains debated. The critical point may be the lack of consensus on MHO’s definition and diagnostic criteria. This study aimed to investigate the association of MHO status with arteriosclerosis-CVD (ASCVD) risk in Chinese under new diagnostic criteria. Participants aged 35–79 in the 2009 China Health and Nutrition Survey cohort were included. The 10-year ASCVD risk was predicted by the prediction for ASCVD risk in China, and participants with a predicted risk of ⩾ 10% were classified into the high-risk group. The Bayesian network (BN) models were constructed to characterize the multivariable probabilistic connections between metabolically obesity phenotypes and ASCVD risk. The 10-year ASCVD risk score and the proportion of individuals at ASCVD high risk were significantly different between metabolically obesity phenotypes (P< 0.001). BN reasoning results showed that MHO individuals were not significantly associated with a 10-year ASCVD risk. Among metabolically unhealthy individuals, the conditional probability of high ASCVD risk increased with the Body Mass Index (BMI), with the conditional probability of high ASCVD risk was 24.63% (95% CI: 22.81–26.55%), 32.97% (95% CI: 30.75–35.27%) and 40.2% (95% CI: 36.64–43.86%) for metabolically unhealthy normal weight (MUNW), metabolically healthy overweight weight (MHOW), and metabolically unhealthy obesity (MUO) group, respectively. Subgroup analysis showed that MHO individuals were at increased risk of CVD compared with metabolically healthy normal weight (MHNW) individuals only in females. These results showed that there was no significant increase in ASCVD risk of MHO phenotype based on the new diagnostic criteria, suggesting that MHO is in a relatively healthy state. [ABSTRACT FROM AUTHOR]

Experimental design allows us to more accurately determine the causal relationship between variables correlated over time as compared to observational design based on conditional probability. Observational design allows us to establish merely a dependency relationships that helps predict the objective variable. However, under certain observational design conditions, it is possible to determine the causal effects of the experimental design without carrying out an intervention. In this work, we present a causal model of higher-order crossed lags capable of inferring causal relationships with hypothetical interventions under an observational design. Additionally, a visualization form is offered that allows us to analyze multiple interventions simultaneously. The methodology is applied to three financial series: the Euro–United States Dollar exchange rate; the Dow Jones Industrial Index; and Gold futures. An analysis of causality concerning their volatilities and differences between the approaches is presented as well as a classic approach of conditioning. Researchers must be cautious in defining the research objective and design for other studies since the approaches lead to very different causal conclusions. The framework presented is expected to be useful in any discipline where one wants to learn What would happen if we intervene? without actually making an intervention. [ABSTRACT FROM AUTHOR]

With the rapid development of industry, the risks factories face are increasing. Therefore, the anomaly detection algorithms deployed in factories need to have high accuracy, and they need to be able to promptly discover and locate the specific equipment causing the anomaly to restore the regular operation of the abnormal equipment. However, the neural network models currently deployed in factories cannot effectively capture both temporal features within dimensions and relationship features between dimensions; some algorithms that consider both types of features lack interpretability. Therefore, we propose a high-precision, interpretable anomaly detection algorithm based on variational autoencoder (VAE). We use a multi-scale local weight-sharing convolutional neural network structure to fully extract the temporal features within each dimension of the multi-dimensional time series. Then, we model the features from various aspects through multiple attention heads, extracting the relationship features between dimensions. We map the attention output results to the latent space distribution of the VAE and propose an optimization method to improve the reconstruction performance of the VAE, detecting anomalies through reconstruction errors. Regarding anomaly interpretability, we utilize the VAE probability distribution characteristics, decompose the obtained joint probability density into conditional probabilities on each dimension, and calculate the anomaly score, which provides helpful value for technicians. Experimental results show that our algorithm performed best in terms of F1 score and AUC value. The AUC value for anomaly detection is 0.982, and the F1 score is 0.905, which is 4% higher than the best-performing baseline algorithm, Transformer with a Discriminator for Anomaly Detection (TDAD). It also provides accurate anomaly interpretation capability. [ABSTRACT FROM AUTHOR]

The transition density of the diffusion process plays an important role in calculating the dynamic characteristics of the underlying variables as well as the model estimation. In this article, we combine the idea of conditional probability density function with non parametric kernel regression, and introduce the kernel estimation of joint density function and marginal density function, then construct the non parametric kernel estimator of the transition density of second-order diffusion process based on discrete observational samples. In order to obtain the asymptotic properties of the new kernel estimator, we analyze the asymptotic expectation and asymptotic variance of the proposed estimator under some mild conditions. Finally, the consistency and asymptotic normality of the new proposed non parametric estimator of the transition density function are proved. [ABSTRACT FROM AUTHOR]

In many machine learning applications, there are many scenarios when performance is not satisfactory by single classifiers. In this case, an ensemble classification is constructed using several weak base learners to achieve satisfactory performance. Unluckily, the construction of the ensemble classification is empirical, i.e., to try an ensemble classification and if performance is not satisfactory then discard it. In this paper, a challenging analytical problem of the estimation of ensemble classification using the prediction performance of the base learners is considered. The proposed formulation is aimed at estimating the performance of ensemble classification without physically developing it, and it is derived from the perspective of probability theory by manipulating the decision probabilities of the base learners. For this purpose, the output of a base learner (which is either true positive, true negative, false positive, or false negative) is considered as a random variable. Then, the effects of logical disjunction-based and majority voting-based decision combination strategies are analyzed from the perspective of conditional joint probability. To evaluate the forecasted performance of ensemble classifier by the proposed methodology, publicly available standard datasets have been employed. The results show the effectiveness of the derived formulations to estimate the performance of ensemble classification. In addition to this, the theoretical and experimental results show that the logical disjunction-based decision outperforms majority voting in imbalanced datasets and cost-sensitive scenarios. [ABSTRACT FROM AUTHOR]

In the literature, modifying the baseline distributions and proposing novel approach of the existing probability models play an important role in the analysis of real-world phenomena as well as the development of new distributions essentially stems from the need to adequately characterize environmental and lifetime events. In this paper, we introduce a new model by applying maximum compound technique to the baseline distributions for modeling more types data used in different case of studies including symmetric, asymmetric, skewed and complex data sets. The proposed model called univariate maximum Harris extended Rayleigh distribution and three parameters characterize it, and it is regarded as strong competitor for widely applied symmetrical and non symmetrical models. The paper offers varied fundamental distributional and mathematical properties of the suggested distribution, such as conditional probability and its expectation, as well as quantile and moment-generating functions. The model's parameters are estimated by applying numerous procedures including maximum likelihood estimation, Expectation–Maximization iterative method and Bayesian approach. Bayesian estimation is performed via various loss functions such as square error, Linex and general entropy functions. As well as, approximate and Bootstrap methods are used to construct the confidence interval for the unknown parameters of the proposed model. Additionally, we illustrated different extensive simulation experiments to see the potential of the suggested estimation methods which give satisfactory performance results. In the end, we applied two real data sets for illustrative purposes. In our illustration, we have compared the practicality of the recommended model with several well-known competing distributions. [ABSTRACT FROM AUTHOR]

A stereotype is a generalization about a class of people which is often used to make probabilistic predictions about individuals within that class. Can stereotypes can be understood as conditional probabilities that distinguish among groups in ways that follow Bayesian posterior prediction? For instance, the stereotype of Germans as industrious can be understood as the conditional probability of someone being industrious given that they are German. Whether such representations follow Bayes’ rule was tested in a replication and extension of past work. Across three studies (N = 2,652), we found that people’s judgments of different social categories were appropriately Bayesian, in that their direct posterior predictions were aligned with what Bayes’ rule suggests they should be. Moreover, across social categories, traits with a high calculated diagnostic ratio generally distinguished stereotypic from non-stereotypic traits. The effects of cognitive ability, political orientation, and motivated stereotyping were also explored. [ABSTRACT FROM AUTHOR]

AbstractThis article proposes a new association statistic for determining whether random variables are statistically independent. The proposed association statistic can also be used to examine the strength of both linear and nonlinear dependency between variables. This statistic is derived by examining how the conditional probabilities of events differ from their corresponding marginal probabilities. The new statistic is defined in terms of the probability of acceptance commonly associated with the Accept–Reject algorithm and has a very simple formula. The simulated density of the probability of acceptance can be used to measure the degree of uncertainty of the estimated values of the new association statistic. The results from simulations as well as examples that employ real data indicate that this new association statistic is very powerful in detecting linear and nonlinear associations between two random variables. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

AbstractThe multinomial probit (MNP) framework is based on a multivariate Gaussian latent structure, allowing for natural extensions to multilevel modeling. Unlike multinomial logistic models, MNP does not assume independent alternatives. Kindo, Wang, and Peña proposed multinomial probit BART (MPBART) to accommodate Bayesian additive regression trees (BART) formulation in MNP. The posterior sampling algorithms for MNP and MPBART are collapsed Gibbs samplers. Because the collapsing augmentation strategy yields a geometric rate of convergence no greater than that of a standard Gibbs sampling step, it is recommended whenever computationally feasible (Liu; Imai and van Dyk). While this strategy necessitates simple sampling steps and a reasonably fast converging Markov chain, the complexity of the stochastic search for posterior trees may undermine its benefit. We address this problem by sampling posterior trees conditional on the constrained parameter space and compare our proposals to that of Kindo, Wang, and Peña, who sample posterior trees based on an augmented parameter space. In terms of MCMC convergence and posterior predictive accuracy, our proposals outperform the augmented tree sampling approach. We also show that the theoretical mixing rates of our proposals are guaranteed to be no greater than the augmented tree sampling approach. Appendices and codes for simulations and demonstrations are available online. [ABSTRACT FROM AUTHOR]

Inverse probability weighting (IPW) is frequently used to reduce or minimize the observed confounding in observational studies. IPW creates a pseudo-sample by weighting each individual by the inverse of the conditional probability of receiving the treatment level that he/she has actually received. In the pseudo-sample there is no variation among the multiple individuals generated by weighting the same individual in the original sample. This would reduce the variability of the data and therefore bias the variance estimate in the target population. Conventional variance estimation methods for IPW estimators generally ignore this underestimation and tend to produce biased estimates of variance. We here propose a more reasonable method that incorporates this source of variability by using parametric bootstrapping based on intra-stratum variability estimates. This approach firstly uses propensity score stratification and intra-stratum standard deviation to approximate the variability among multiple individuals generated based on a single individual whose propensity score falls within the corresponding stratum. The parametric bootstrapping is then used to incorporate the target variability by re-generating outcomes after adding a random error term to the original data. The performance of the proposed method is compared with three existing methods including the naïve model-based variance estimator, the nonparametric bootstrap variance estimator, and the robust variance estimator in the simulation section. An example of patients with sarcopenia is used to illustrate the implementation of the proposed approach. According to the results, the proposed approach has desirable statistical properties and can be easily implemented using the provided R code. [ABSTRACT FROM AUTHOR]

Modelling one‐to‐many type mappings in problems with a temporal component can be challenging. Backpropagation is not applicable to networks that perform discrete sampling and is also susceptible to gradient instabilities, especially when applied to longer sequences. In this paper, we propose two recurrent neural network architectures that leverage stochastic units and mixture models, and are trained with target propagation. We demonstrate that these networks can model complex conditional probability distributions, outperform backpropagation‐trained alternatives, and do not rapidly degrade with increased time horizons. Our main contributions consist of the design and evaluation of the architectures that enable the networks to solve multi‐model problems with a temporal dimension. This also includes the extension of the target propagation through time algorithm to handle stochastic neurons. The use of target propagation provides an additional computational advantage, which enables the network to handle time horizons that are substantially longer compared to networks fitted using backpropagation. [ABSTRACT FROM AUTHOR]

The obligated implementation of the Electronic Chart Display and Information System (ECDIS) began ten years ago, and during this time, we could observe different types of familiarization with the system through its users. The incorrect use of the ECDIS is a problem recognized by the International Hydrographic Organization (IHO), and it is caused by disparate levels of education and the quality of courses. Another aspect of the recognized problem is the unequal length of the ECDIS specific course training period through which users acquire type-specific familiarization with the system. Switching between different types of ECDISs makes users more prone to errors until they become fully familiar with the new product system. This paper analyzes seafarers' knowledge and ECDIS usage over a 6-year period. Based on obtained data, models that enable risk analysis based on conditional probability were created. This paper presents the use of Bayesian modeling to reduce errors in maritime accidents caused by inadequate use of the ECDIS. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)