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Processes or products are typically complex systems with numerous interrelated procedures and interdependent components. This results in complex relationships between responses and input factors, as well as complex nonlinear correlations among multiple responses. If the two types of complex correlations in the quality design cannot be properly dealt with, it will affect the prediction accuracy of the response surface model, as well as the accuracy and reliability of the recommended optimal solutions. In this paper, we combine kernel trick-based kernel principal component analysis, spline-based Bayesian semiparametric additive model, and normal boundary intersection-based evolutionary algorithm to address these two types of complex correlations. The effectiveness of the proposed method in modeling and optimisation is validated through a simulation study and a case study. The results show that the proposed Bayesian semiparametric additive model can better describe the process relationships compared to least squares regression, random forest regression, and support vector basis regression, and the proposed multi-objective optimisation method performs well on several indicators mentioned in the paper. [ABSTRACT FROM AUTHOR]

Standard choice-based conjoint models often ignore or insufficiently approximate consumers' budget constraints, despite the prominent role of budget constraints in economic theory. The authors offer a theoretically motivated improvement to the choice-based conjoint model that is especially appropriate for high-ticket durable goods and develop a Bayesian method for the inference of unobserved budget constraints. The proposed method leverages respondents' stated budget constraints that suffer from measurement error and respondents' financial demographic variables as additional information to reduce the dependency on functional form assumptions in the estimation. The authors show that accounting for budget constraints substantially increases model fit and the accuracy of competitive pricing in an industry-grade discrete-choice experiment on consumer preferences for high-end laptops. The proposed model performs better than the canonical linear price benchmark model, which is not flexible enough to approximate budget constraints. In theory, more flexible utility specifications, such as the nonlinear dummy price model, can approximate consumers' budget constraints. However, they perform poorly when only finite data are available. The authors conclude that applied researchers in industry and academia will benefit from having a better tool for estimating budgets in high-ticket categories. [ABSTRACT FROM AUTHOR]

Individual difference exploration of cognitive domains is predicated on being able to ascertain how well performance on tasks covary. Yet, establishing correlations among common inhibition tasks such as Stroop or flanker tasks has proven quite difficult. It remains unclear whether this difficulty occurs because there truly is a lack of correlation or whether analytic techniques to localize correlations perform poorly real-world contexts because of excessive measurement error from trial noise. In this paper, we explore how well correlations may localized in large data sets with many people, tasks, and replicate trials. Using hierarchical models to separate trial noise from true individual variability, we show that trial noise in 24 extant tasks is about 8 times greater than individual variability. This degree of trial noise results in massive attenuation in correlations and instability in Spearman corrections. We then develop hierarchical models that account for variation across trials, variation across individuals, and covariation across individuals and tasks. These hierarchical models also perform poorly in localizing correlations. The advantage of these models is not in estimation efficiency, but in providing a sense of uncertainty so that researchers are less likely to misinterpret variability in their data. We discuss possible improvements to study designs to help localize correlations.

Air pollution stands as an environmental risk to child mental health, with proven relationships hitherto observed only in urban areas. Understanding the impact of pollution in rural settings is equally crucial. The novelty of this article lies in the study of the relationship between air pollution and behavioural and developmental disorders, attention deficit hyperactivity disorder (ADHD), anxiety, and eating disorders in children below 15 living in a rural area. The methodology combines spatio-temporal models, Bayesian inference and Compositional Data (CoDa), that make it possible to study areas with few pollution monitoring stations. Exposure to nitrogen dioxide (NO2), ozone (O3), and sulphur dioxide (SO2) is related to behavioural and development disorders, anxiety is related to particulate matter (PM10), O3 and SO2, and overall pollution is associated to ADHD and eating disorders. To sum up, like their urban counterparts, rural children are also subject to mental health risks related to air pollution, and the combination of spatio-temporal models, Bayesian inference and CoDa make it possible to relate mental health problems to pollutant concentrations in rural settings with few monitoring stations. Certain limitations persist related to misclassification of exposure to air pollutants and to the covariables available in the data sources used. [ABSTRACT FROM AUTHOR]

The generalized extreme value (GEV) distribution is a popular model for analyzing and forecasting extreme weather data. To increase prediction accuracy, spatial information is often pooled via a latent Gaussian process (GP) on the GEV parameters. Inference for GEV‐GP models is typically carried out using Markov Chain Monte Carlo (MCMC) methods, or using approximate inference methods such as the integrated nested Laplace approximation (INLA). However, MCMC becomes prohibitively slow as the number of spatial locations increases, whereas INLA is applicable in practice only to a limited subset of GEV‐GP models. In this article, we revisit the original Laplace approximation for fitting spatial GEV models. In combination with a popular sparsity‐inducing spatial covariance approximation technique, we show through simulations that our approach accurately estimates the Bayesian predictive distribution of extreme weather events, is scalable to several thousand spatial locations, and is several orders of magnitude faster than MCMC. A case study in forecasting extreme snowfall across Canada is presented. [ABSTRACT FROM AUTHOR]

Differences in valenced self‐evaluation refer to positive and negative coexistence in the process of self‐evaluation, while there is a clear difference in cognitive processes. The present study aimed to uncover the differences in the latent cognitive parameters (e.g., processing speed) in valenced self‐evaluation using the hierarchical drift‐diffusion model in two independent experiments. A self‐referential decision‐making task was applied in both experiments, and a self‐descriptiveness task plus the rating of related emotions (e.g., pride and shame) were also used but only in Experiment 2. Results of Experiments 1 & 2 showed a faster processing speed for accepting positive attributes and longer times for encoding and response execution in negative self‐evaluation. Moreover, Experiment 2 found cognitive parameters had predictive effects on subsequent decisional outcomes such as self‐descriptiveness and self‐related emotions via Bayesian inference. The current study provided findings that help to understand the cognitive mechanism behind self‐positivity and self‐accuracy biases. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) have garnered significant attention due to their notable energy efficiency. However, conventional SNNs rely on spike firing frequency to encode information, necessitating a fixed sampling time and leaving room for further optimization. This study presents a novel approach to reduce sampling time and conserve energy by extracting early prediction results from the intermediate layer of the network and integrating them with the final layer's predictions in a Bayesian fashion. Experimental evaluations conducted on image classification tasks using MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate the efficacy of our proposed method when applied to VGGNets and ResNets models. Results indicate a substantial energy reduction of 38.8% in VGGNets and 48.0% in ResNets, illustrating the potential for achieving significant efficiency gains in spiking neural networks. These findings contribute to the ongoing research in enhancing the performance of SNNs, facilitating their deployment in resource-constrained environments. Our code is available on GitHub: https://github.com/hanebarla/BayesianSpikeFusion. [ABSTRACT FROM AUTHOR]

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making. [ABSTRACT FROM AUTHOR]

On-orbit configuration estimation is one of the most important tasks for large-scale distributed spacecraft systems, yet incomplete measurement topologies limit the acquisition of partial inter-satellite relative navigation information. In this article, an on-orbit configuration estimation algorithm based on augmented sensing is proposed, which enables each spacecraft to extend the estimation range beyond the observation range without any communication overhead. Referring to the Bayesian inference process, the cooperative behavior of the large-scale distributed spacecraft system is applied to infer relative motion states of unobservable spacecrafts. Under the framework of scaled unscented transformations (SUT), we achieve high-accuracy configuration estimation for the large-scale distributed spacecraft system. Finally, the proposed algorithm is verified in a high-orbiting gravitational wave detector constellation with inter-satellite laser interferometers. [ABSTRACT FROM AUTHOR]

We provide Bayesian inference in the context of Least Median of Squares and Least Trimmed Squares, two well-known techniques that are highly robust to outliers. We apply the new Bayesian techniques to linear models whose errors are independent or AR and ARMA. Model comparison is performed using posterior model probabilities, and the new techniques are examined using Monte Carlo experiments as well as an application to four portfolios of asset returns. [ABSTRACT FROM AUTHOR]

Introduction: In virtual bioequivalence (VBE) assessments, pharmacokinetic models informed with in vitro data and verified with small clinical trials' data are used to simulate otherwise unfeasibly large trials. Simulated VBE trials are assessed in a frequentist framework as if they were real despite the unlimited number of virtual subjects they can use. This may adequately control consumer risk but imposes unnecessary risks on producers. We propose a fully Bayesian model-integrated VBE assessment framework that circumvents these limitations. Methods: We illustrate our approach with a case study on a hypothetical paliperidone palmitate (PP) generic long-acting injectable suspension formulation using a validated population pharmacokinetic model published for the reference formulation. BE testing, study power, type I and type II error analyses or their Bayesian equivalents, and safe-space analyses are demonstrated. Results: The fully Bayesian workflow is more precise than the frequentist workflow. Decisions about bioequivalence and safe space analyses in the two workflows can differ markedly because the Bayesian analyses are more accurate. Discussion: A Bayesian framework can adequately control consumer risk and minimize producer risk. It rewards data gathering and model integration to make the best use of prior information. The frequentist approach is less precise but faster to compute, and it can still be used as a first step to narrow down the parameter space to explore in safe-space analyses. [ABSTRACT FROM AUTHOR]

Despite the progress in medical data collection the actual burden of SARS‐CoV‐2 remains unknown due to under‐ascertainment of cases. This was apparent in the acute phase of the pandemic and the use of reported deaths has been pointed out as a more reliable source of information, likely less prone to under‐reporting. Since daily deaths occur from past infections weighted by their probability of death, one may infer the total number of infections accounting for their age distribution, using the data on reported deaths. We adopt this framework and assume that the dynamics generating the total number of infections can be described by a continuous time transmission model expressed through a system of nonlinear ordinary differential equations where the transmission rate is modeled as a diffusion process allowing to reveal both the effect of control strategies and the changes in individuals behavior. We develop this flexible Bayesian tool in Stan and study 3 pairs of European countries, estimating the time‐varying reproduction number (Rt$$ {R}_t $$) as well as the true cumulative number of infected individuals. As we estimate the true number of infections we offer a more accurate estimate of Rt$$ {R}_t $$. We also provide an estimate of the daily reporting ratio and discuss the effects of changes in mobility and testing on the inferred quantities. [ABSTRACT FROM AUTHOR]

Marine ecosystems are experiencing growing pressure from multiple threats caused by human activities, with far‐reaching consequences for marine food webs. Determining the effects of multiple stressors is complex, in part, as they can affect different aspects of biological organisation (behaviour, individual traits and demographic rates). Determining the combined effects of stressors, through different biological pathways, is key to predict the consequences for the viability of populations threatened by global change. Due to their position in the food chain, top predators such as seabirds are considered more sensitive to environmental changes. Climate change is affecting the prey resources available for seabirds, through bottom‐up effects, while organic pollutants can bioaccumulate in food chains with the greatest impacts on top predators. However, knowledge of their combined effects on population dynamics is scarce. Using a path analysis, we quantify the effects of climate change and pollution on the survival of adult great black‐backed gulls, both directly and through effects of individuals' body mass. Warmer ocean temperatures in gulls' winter foraging areas in the North Sea were correlated with higher survival, potentially explained by shifts in prey availability associated with global climate change. We also found support for indirect negative effects of organochlorines, highly toxic pollutants to seabirds, on survival, which acted, in part, through a negative effect on body mass. The results from this path analysis highlight how, even for such long‐lived species where variance in survival tends to be limited, two stressors still have had a marked influence on adult survival and illustrate the potential of path models to improve predictions of population variability under multiple stressors. [ABSTRACT FROM AUTHOR]

Understanding the emergence of symbol systems, especially language, requires the construction of a computational model that reproduces both the developmental learning process in everyday life and the evolutionary dynamics of symbol emergence throughout history. This study introduces the collective predictive coding (CPC) hypothesis, which emphasizes and models the interdependence between forming internal representations through physical interactions with the environment and sharing and utilizing meanings through social semiotic interactions within a symbol emergence system. The total system dynamics is theorized from the perspective of predictive coding. The hypothesis draws inspiration from computational studies grounded in probabilistic generative models and language games, including the Metropolis-Hastings naming game. Thus, playing such games among agents in a distributed manner can be interpreted as a decentralized Bayesian inference of representations shared by a multi-agent system. Moreover, this study explores the potential link between the CPC hypothesis and the free-energy principle, positing that symbol emergence adheres to the society-wide free-energy principle. Furthermore, this paper provides a new explanation for why large language models appear to possess knowledge about the world based on experience, even though they have neither sensory organs nor bodies. This paper reviews past approaches to symbol emergence systems, offers a comprehensive survey of related prior studies, and presents a discussion on CPC-based generalizations. Future challenges and potential cross-disciplinary research avenues are highlighted. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined combinatorial ABC-SMC. Inference of this type is used in situations where there does not exist a closed form of the associated likelihood function, which is replaced by a simulating model capable of producing artificial data. In the literature, conventional ABC-SMC is utilised to perform inference on continuous parameters. The novel scheme presented here has been developed to perform inference on parameters that are high-dimensional binary, rather than continuous. By altering the form of the proposal distribution from which to sample candidates in subsequent iterations (referred to as waves), high-dimensional binary variables may be targeted and inferred by the scheme. The efficacy of the proposed scheme is demonstrated through application to vibration data obtained in a structural dynamics experiment on a fibre-optic sensor simulated as a finite plate with uncertain boundary conditions at its edges. Results indicate that the method provides sound inference on the plate boundary conditions, which is validated through subsequent application of the method to multiple vibration datasets. Comparisons between appropriate forms of the metric function used in the scheme are also developed to highlight the effect of this element in the schemes convergence. [ABSTRACT FROM AUTHOR]

Workload management is a critical concern in shared control of unmanned ground vehicles. In response to this challenge, prior studies have developed methods to estimate human operators' workload by analyzing their physiological data. However, these studies have primarily adopted a single-model-single-feature or a single-model-multiple-feature approach. The present study proposes a Bayesian inference model to estimate workload, which leverages different machine learning models for different features. We conducted a human subject experiment with 24 participants, in which a human operator teleoperated a simulated High Mobility Multipurpose Wheeled Vehicle (HMMWV) with the help from an autonomy while performing a surveillance task simultaneously. Participants' eye-related features, including gaze trajectory and pupil size change, were used as the physiological input to the proposed Bayesian inference model. Results show that the Bayesian inference model achieves a 0.823 F1 score, 0.824 precision, and 0.821 recall, outperforming the single models. [ABSTRACT FROM AUTHOR]

Small‐angle scattering (SAS) is a key experimental technique for analyzing nanoscale structures in various materials. In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting. This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models. The performance of the method is assessed through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that this proposed analysis approach yields highly accurate and interpretable results. The ability of the method to analyze a range of mixing ratios and particle size ratios for mixed components is also discussed, along with its precision in model evaluation by the degree of fitting. The proposed method effectively facilitates quantitative analysis of nanoscale sample structures in SAS, which has traditionally been challenging, and is expected to contribute significantly to advancements in a wide range of fields. [ABSTRACT FROM AUTHOR]

Missing data reduce the representativeness of the sample and can lead to inference problems. In this article, we apply the Bayesian jackknife empirical likelihood (BJEL) method for inference on data that are missing at random, as well as for causal inference. The semiparametric fractional imputation estimator, propensity score‐weighted estimator, and doubly robust estimator are used for constructing the jackknife pseudo values, which are needed for conducting BJEL‐based inference with missing data. Existing methods, such as normal approximation and JEL, are compared with the BJEL approach in a simulation study. The proposed approach shows better performance in many scenarios in terms of credible intervals. Furthermore, we demonstrate the application of the proposed approach for causal inference problems in a study of risk factors for impaired kidney function. [ABSTRACT FROM AUTHOR]

Spatial information on forest composition is invaluable for achieving scientific, ecological, and management objectives and for monitoring multiple changes in forest ecosystems. The increased flow of optical satellite data provides new opportunities to improve tree species mapping. However, the accuracy of such maps is affected by training data, and in particular on the homogeneity of individual classes. Thus, we evaluated the effect of data homogeneity in tree species classification. We performed tree species classification by considering different ways to partition data into tree species classes. The class sets considered were (i) only mixed coniferous and mixed deciduous forest classes, (ii) single-species classes, (iii) single-species, mixed coniferous and mixed deciduous classes, and (iv) single-species, mixed coniferous and mixed deciduous classes and a true mixed class. Using data from the Swedish National Forest Inventory, we varied the threshold that defined dominating species. Tree species were classified for a study area in central Sweden using Sentinel-2 data and two classification approaches: Bayesian inference and random forest (RF). Images were selected by class separability and the most informative images based on variable selection with RF. The most informative images tended to be selected by both methods. However, in forests with tree species of similar spectral behaviour, image selection on the basis of class separability was found to be more reliable. More accurate classification results were achieved as the number of classes decreased and the threshold of plot purity increased. The Bayesian classification approach of only mixed coniferous and mixed deciduous classes gave the highest OA, always greater than 90%. When discriminating between pure plots of Birch (Betula spp.), Spruce (Picea abies), Scots pine (Pinus sylvestris) and Lodgepole pine (Pinus contorta), the best OA values were 84% for Bayesian and 80% for RF. In more complicated scenarios, RF resulted in higher overall accuracies (OA). [ABSTRACT FROM AUTHOR]

Multilevel Stein variational gradient descent is a method for particle-based variational inference that leverages hierarchies of surrogate target distributions with varying costs and fidelity to computationally speed up inference. The contribution of this work is twofold. First, an extension of a previous cost complexity analysis is presented that applies even when the exponential convergence rate of single-level Stein variational gradient descent depends on iteration-varying parameters. Second, multilevel Stein variational gradient descent is applied to a large-scale Bayesian inverse problem of inferring discretized basal sliding coefficient fields of the Arolla glacier ice. The numerical experiments demonstrate that the multilevel version achieves orders of magnitude speedups compared to its single-level version. [ABSTRACT FROM AUTHOR]

More than three dozen submillisecond events of ELVES type ("elves"), which are the result of the interaction of the front of an electromagnetic pulse from a lightning discharge and the lower layer of the ionosphere, have been identified in the data of a UV Atmosphere orbital multichannel detector (Mini-EUSO). Each event has a characteristic annular glow pattern and occupies a significant part of the detector's field of view, and the signal in a separate channel has an asymmetric profile with a pronounced peak. The distribution of peak times contains information about both the localization of the discharge and the altitude of the glow. In this paper, we propose a Bayesian (probabilistic) model for reconstructing ELVES events, implemented using probabilistic programming methods in PyMC-5. The capabilities of the model for determining the position of the discharge are shown using the example of several events. Methods for modifying the model to restore the discharge orientation and refine the glow height are outlined. [ABSTRACT FROM AUTHOR]

Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression hierarchical model for analysing brain connectivity within resting-state functional magnetic resonance imaging, and apply it to simulated data and a real data set with subjects in different groups. Our approach models functional and effective connectivity simultaneously and allows for both group- and single-subject inference. We combine analytical marginalization with Hamiltonian Monte Carlo to obtain highly efficient posterior sampling. We show that our model gives similar inference for effective connectivity compared to models with a common covariance matrix to all subjects, but more accurate inference for functional connectivity between regions compared to models with more restrictive covariance structures. A Stan implementation of our model is available on GitHub. [ABSTRACT FROM AUTHOR]

This paper employs Bayesian model averaging (BMA) and uses posterior inclusion probability (PIP) values to evaluate which financial inclusion indicators, dimensions, and other determinants of income inequality should be considered in an empirical specification assessing the relationship between financial inclusion and income inequality, given model uncertainty. The results show that for the lower income country group, financial access and usage indicators are the most relevant financial inclusion indicators. For financial inclusion dimension, we find evidence on the importance of financial access. However, the results for upper income country group are different, suggesting the need to differentiate between the two groups in assessing the importance of financial inclusion on income inequality. These results suggest that theoretical models linking financial inclusion and income inequality could well focus on the role of financial access by providing theoretical foundations on the mechanics as to how this dimension of financial inclusion impact income inequality particularly for lower income countries. [ABSTRACT FROM AUTHOR]

Stability analyses of tailings dams often utilize advanced constitutive models that require the calibration of many parameters. Yet, there remains limited research on the implications of parameter uncertainties within these constitutive models on the safety factor of tailings dams. This study successfully investigated the impact of uncertainty in critical state parameters of the NorSand model on the stability of a tailings dam. To achieve this goal, field and laboratory tests characterized the materials constituting the geotechnical profile of the dam, involving residual shale soils, alluvial soils, and granular-textured tailings. These tests were included in the finite element (FE) program PLAXIS 2D to estimate several parameters of the constitutive models that characterized the materials of the dam: Hardening Soil, Mohr–Coulomb, and NorSand. Monte Carlo simulations of FE analyses were conducted considering the construction period of the dam spanning 2500 days. Furthermore, the critical state line (CSL) parameters employed within the NorSand model were reconciled with void ratio data for the uncompacted tailings utilizing a Bayesian framework. The Bayesian framework allowed us to investigate: (i) the statistical distribution of the critical state parameters, (ii) confidence intervals for horizontal and vertical displacements, (iii) shear strains at various monitoring points across the tailings dam, (iv) and the marginal distribution of the factor of safety. The findings reveal a strong correlation between the three CSL parameters, which presented poorly defined statistical distributions. Nonetheless, the power law equation used to model the CSL closely matches laboratory data from triaxial tests (R2 = 0.97). Uncertainties in the CSL, especially at lower stress levels, contribute to settlement prediction uncertainties. Monitoring points at the structure's base showed strains that could induce downstream slope instability and potential liquefaction failure. The histogram of the safety factors remained narrow, normally distributed, ranging from 1.39 to 1.47. This work is important because it explicitly addresses the uncertainty in the critical state parameters of the NorSand model within geotechnical analysis. [ABSTRACT FROM AUTHOR]

A nematological survey was conducted between 2021 and 2022 in banana fields distributed in two major banana-producing regions in the western coast of Syria. As a result, six populations of Xiphinema spp. identified as X. diffusum, X. pachtaicum, X. simile, X. vuittenezi and X. zagrosense were isolated from the rhizosphere of banana plants (Musa sp.) and characterized using morphological and molecular data based on two rRNA genes (D2–D3 expansion segments of the 28S, and ITS fragments) and partial region of the cytochrome oxidase I gene (COI mtDNA) sequences. Moreover, the molecular identification of the endosymbionts of these populations was also performed using the complete 16S rDNA gene. The phylogenetic relationships of the recovered species of nematodes and respective endosymbionts were reconstructed. Candidatus Xiphinematobacter sp. (OR196969; OR196971) and Ca. Xiphinematobacter sp. (OR196970) were detected in X. diffusum and X. simile, respectively, and clustered together with other Ca. Xiphinematobacter sp.A and sp.I respectively. To our knowledge, this is the first report of X. diffusum, X. simile, X. vuittenezi and X. zagrosense parasitizing banana in Syria, extending the geographical distribution of these species within the Mediterranean Basin. [ABSTRACT FROM AUTHOR]

Benchmark analysis is a general risk estimation strategy for identifying the benchmark dose (BMD) past which the risk of exhibiting an adverse environmental response exceeds a fixed, target value of benchmark response. Estimation of BMD and of its lower confidence limit (BMDL) is well understood for the case of an adverse response to a single stimulus. In many environmental settings, however, one or more additional, secondary, qualitative factor(s) may collude to affect the adverse outcome, such that the risk changes with differential levels of the secondary factor. Bayesian methods for estimation of the BMD and BMDL have grown in popularity, and a large variety of candidate dose–response models is available for applying these methods. This article applies Bayesian strategies to a mixed‐factor setting with a secondary qualitative factor possessing two levels to derive two‐factor Bayesian BMDs and BMDLs. We present reparameterized dose–response models that allow for explicit use of prior information on the target parameter of interest, the BMD. We also enhance our Bayesian estimation technique for BMD analysis by applying Bayesian model averaging to produce the BMDs and BMDLs, overcoming associated questions of model adequacy when multimodel uncertainty is present. An example from environmental carcinogenicity testing illustrates the calculations. [ABSTRACT FROM AUTHOR]

Political leaders articulate themselves best via speeches and/or writings across diverse media (print or electronic) for campaigning, pitching their stand, confronting the opponent/s, impressing upon the citizens, penning down their biographies, and the like. While making speeches, politicians evince provocative sentiments themselves that are likely to move the audience-that is the prime objective of any orator. Concomitantly, however, the politicians make speeches charged with emotions to drive home a point. The present study seeks to hinge itself upon the speeches of Volodymyr Oleksandrovych Zelenskyy, the Ukrainian President, who is embroiled in a war with Russia since February 2022. Specifically, sentiment analysis was done to understand the dynamics of emotions that wavered with the progress of war. Computational text analysis of speeches for a specified period (24 February 2022 until 24 July 2022) shows that sentiments appear to increase over a period of time wherein the best predictor, in our Bayesian regression models, for a change in Zelenskyy's sentiment between today and tomorrow is his "present" sentiment-the sentiment that he evinces "today." Implicitly, if we detect a high/low positive sentiment "today," we would expect to see a strong mean regression such that tomorrow's sentiment should be close to neutral. Findings suggest that in contrast with the general observation that peculiar war events tend to have great power in explaining changes in sentiments, the same was found only to be an ancillary factor in the present study. The study is rounded off with further research pointers with practitioner implications. [ABSTRACT FROM AUTHOR]

Euro area real‐time density forecasts from three dynamic stochastic general equilibrium (DSGE) and three Bayesian vector autoregression (BVAR) models are compared with six combination methods over the sample 2001Q1–2019Q4. The terms information and observation lag are introduced to distinguish time shifts between data vintages and actuals used to compute model weights and compare the forecast, respectively. Bounds for finite mixture combinations are presented, allowing for benchmarking them given the models. Empirically, combinations with limited weight variation often improve upon the individual models for the output and the joint forecasts with inflation. This reflects overconfident BVAR forecasts before the Great Recession. For inflation, a BVAR model typically performs best. [ABSTRACT FROM AUTHOR]

Brute force cross-validation (CV) is a method for predictive assessment and model selection that is general and applicable to a wide range of Bayesian models. Naive or 'brute force' CV approaches are often too computationally costly for interactive modeling workflows, especially when inference relies on Markov chain Monte Carlo (MCMC). We propose overcoming this limitation using massively parallel MCMC. Using accelerator hardware such as graphics processor units, our approach can be about as fast (in wall clock time) as a single full-data model fit. Parallel CV is flexible because it can easily exploit a wide range data partitioning schemes, such as those designed for non-exchangeable data. It can also accommodate a range of scoring rules. We propose MCMC diagnostics, including a summary of MCMC mixing based on the popular potential scale reduction factor ( R ^ ) and MCMC effective sample size ( ESS ^ ) measures. We also describe a method for determining whether an R ^ diagnostic indicates approximate stationarity of the chains, that may be of more general interest for applications beyond parallel CV. Finally, we show that parallel CV and its diagnostics can be implemented with online algorithms, allowing parallel CV to scale up to very large blocking designs on memory-constrained computing accelerators. [ABSTRACT FROM AUTHOR]

Many clinical trials generate both longitudinal biomarker and time‐to‐event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well‐established methods exist for analyzing such data either sequentially (two‐stage models) or simultaneously (joint models). Two‐stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time‐consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two‐stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results. [ABSTRACT FROM AUTHOR]

AbstractDesign of process control scheme is critical for quality assurance to reduce variations in manufacturing systems. Taking semiconductor manufacturing as an example, extensive literature focuses on control optimization based on certain process models (usually linear models), which are obtained by experiments before a manufacturing process starts. However, in real applications, pre-defined models may not be accurate, especially for a complex manufacturing system. To tackle model inaccuracy, we propose a model-free reinforcement learning (MFRL) approach to conduct experiments and optimize control simultaneously according to real-time data. Specifically, we design a novel MFRL control scheme by updating the distribution of disturbances using Bayesian inference to reduce their large variations during manufacturing processes. As a result, the proposed MFRL controller is demonstrated to perform well in a nonlinear chemical mechanical planarization (CMP) process when the process model is unknown. Theoretical properties are guaranteed when disturbances are additive. The numerical studies also demonstrate the efficiency of our methodology. [ABSTRACT FROM AUTHOR]

Estimating the COVID‐19 infection fatality rate, inferring the latent incidence and predicting the future epidemic evolution are critical to public health surveillance, but often challenging due to limited data availability or quality. Recently, a Bayesian framework combining time series deconvolution of deaths with a parametric Susceptible–Infectious–Recovered (SIR) model was proposed by Irons and Raftery, 2021. We assess the parameter identifiability of the model using the profile likelihood approach and simulations, when only the time series of deaths and seroprevalence survey data are available. The robustness of the model to the more complex but also more realistic Susceptible–Exposed–Infectious–Recovered (SEIR)‐based epidemics is evaluated through simulations; the influence of potential biases in the serosurveys on the inference is also investigated. We use a stationary first‐order autoregressive prior to account for the variability of transmission rate over time. The results suggest that the model is relatively robust to SEIR‐based epidemics, especially when the reproductive number is low, given sufficient information from serosurveys or priors. However, the lack of parameter identifiability under limited data availability cannot be neglected. We apply the model to infer the COVID‐19 infections in Ontario and Quebec, Canada during the Omicron era. [ABSTRACT FROM AUTHOR]

Cities are complex systems influenced by a multitude of factors, encompassing society, economy, culture, and environment. These factors make urban development highly vulnerable to various disturbances. Communities work as the fundamental building blocks of a city and directly impact both its social structure and spatial layout. Moreover, urban planning and policies play a crucial role in shaping the development trajectory of communities and the living environment for residents. This study aims to develop a Bayesian and analytic hierarchy process (BAHP)-based multilevel community resilience evaluation method to assess the ability of the community system to withstand disturbances and recover from them. First, the proposed method establishes a comprehensive assessment index system that can evaluate social and environmental resilience as well as institutional and managerial resilience at multiple levels. This system serves as a quantitative decision-making tool to elucidate the impact of various factors on community resilience. Furthermore, the "relative demand coefficient" (RDC) is proposed to compare different communities' resilience by using Bayesian inference to determine its most probable value (MPV). To validate the applicability of the proposed method, an empirical study was conducted in the Dafapu community located in the Longgang District of Shenzhen. Meanwhile, a simulated virtual community is employed for comparison with the Dafapu community as an illustrative example showcasing the proposed method's superior performance after integrating the RDC. The empirical study demonstrates that the proposed BAHP-based method can effectively and quantitatively highlight the recovery capabilities and limitations for different communities in various dimensions while providing a clear direction for enhancing urban community resilience. This research contributes new insights to the theory, provides a practical tool to quantify community resilience, and offers a viable path for the actual enhancement of community resilience. [ABSTRACT FROM AUTHOR]

We present a novel procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation (ABC). Unlike previous procedures, our proposal is based on the asymptotic properties of ABC. We demonstrate theoretically, and empirically that our procedure can consistently detect the presence of model misspecification. The examples demonstrate that our proposal shows good finite-sample properties, outperforming existing approaches. An empirical application to modeling exchange rate log returns using a g-and-k distribution completes the article. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Sampling-based algorithms are classical approaches to perform Bayesian inference in inverse problems. They provide estimators with the associated credibility intervals to quantify the uncertainty on the estimators. Although these methods hardly scale to high dimensional problems, they have recently been paired with optimization techniques, such as proximal and splitting approaches, to address this issue. Such approaches pave the way to distributed samplers, splitting computations to make inference more scalable and faster. We introduce a distributed Split Gibbs sampler (SGS) to efficiently solve such problems involving distributions with multiple smooth and non-smooth functions composed with linear operators. The proposed approach leverages a recent approximate augmentation technique reminiscent of primal-dual optimization methods. It is further combined with a block-coordinate approach to split the primal and dual variables into blocks, leading to a distributed block-coordinate SGS. The resulting algorithm exploits the hypergraph structure of the involved linear operators to efficiently distribute the variables over multiple workers under controlled communication costs. It accommodates several distributed architectures, such as the Single Program Multiple Data and client-server architectures. Experiments on a large image deblurring problem show the performance of the proposed approach to produce high quality estimates with credibility intervals in a small amount of time. to reproduce the experiments is available online. [ABSTRACT FROM AUTHOR]

Simulation-based optimization is an effective solution to complex transportation problems relying on stochastic simulations. However, existing studies generally perform a fixed number of evaluations for each decision vector across the design space, overlooking simulation heteroscedasticity and its effects on solution efficiency and robustness. In this paper, we treat the number of evaluations as an adaptive variable depending upon the simulation heteroscedasticity and the potential optimality of each decision vector. A statistical method, which automatically determines the variable number of evaluations, is presented for a range of derivative-free optimization methods. By fusing Bayesian inference with the probability of correct selection, it permits adaptive allocation of budgeted computational resources to achieve improved solution efficiency and robustness. The method is integrated with the deterministic global optimizer DIviding RECTangles (DIRECT) to yield NoisyDIRECT as a continuous simulation-based robust optimization method (which is open sourced). The key properties of the method are proved and discussed. Numerical experiments on difficult test functions are first conducted to verify the improvement of NoisyDIRECT compared with DIRECT and Bayesian optimization. Given the same computational budget, NoisyDIRECT can better locate the global optimum than the other two alternatives. Applications to representative simulation-based transportation problems, including an M/M/1 queueing problem and a parking pricing problem, are then presented. The results demonstrate the ability of NoisyDIRECT to pinpoint the optimal solution via adaptive computational resources allocation, achieving the desired level of robustness. Funding: This work was supported by the Youth Program [Grant 52102375] and the Key Project [Grant 52131203] of the National Natural Science Foundation of China, the Youth Program [Grant BK20210247] of the Natural Science Foundation of Jiangsu Province, and the High-Level Personnel Project of Jiangsu Province [Grant JSSCBS20220099]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0485. [ABSTRACT FROM AUTHOR]

Ensemble methods have become ubiquitous for the solution of Bayesian inference problems. State-of-the-art Langevin samplers such as the ensemble Kalman sampler (EKS) or the affine invariant Langevin dynamics (ALDI) rely on successive evaluations of the forward model or its gradient. A main drawback of these methods hence is their vast number of required forward calls as well as their possible lack of convergence in the case of more involved posterior measures such as multimodal distributions. The goal of this paper is to address these challenges to some extent. First, several possible adaptive ensemble enrichment strategies that successively enlarge the number of particles in the underlying Langevin dynamics are discussed that in turn lead to a significant reduction of the total number of forward calls. Second, analytical consistency guarantees of the ensemble enrichment method are provided for linear forward models. Third, to address more involved target distributions, the method is extended by applying adapted Langevin dynamics based on a homotopy formalism for which convergence is proved. Finally, numerical investigations of several benchmark problems illustrate the possible gain of the proposed method, comparing it to state-of-the-art Langevin samplers. [ABSTRACT FROM AUTHOR]

Camera traps have been widely used in wildlife research, offering significant potential for monitoring species interactions at ephemeral resources. However, raw data obtained from camera traps often face limitations due to observation censoring, where resource consumption by dominant animals may obscure potential resource use by less dominant animals. We extended time‐to‐detection occupancy modeling to quantify interspecific consumptive competition and redundancy of ecosystem functions through consumption between two species, while accounting for observation censoring. By treating resource use by rival species as censored data, we estimated the proportion of resources potentially used in the absence of rival species and calculated the loss caused by the rival species, which is defined as "Competition Intensity Index." We also defined the Unique Functional Contribution, which represents the net functional loss when a species is removed, calculated by excluding the contribution potentially substituted by the other species. We also considered resource degradation and computed the quantity of resources acquired by each species. This established framework was applied to predation data on bird nests by alien squirrels and other predators (Case 1) as well as scavenging on mammalian carcasses by two carnivores (Case 2). In Case 1, the introduction of squirrels significantly affected the breeding success of birds. Although nests were being preyed upon by native crows also, our model estimated that Unique Functional Contribution by the squirrels was 0.47. This means that, by eradicating the squirrels, the reproductive success of the birds could potentially increase by as much as 47%. In Case 2, the Competition Intensity Index for foxes was 0.17, whereas that for raccoon dogs was 0.46, suggesting an asymmetric effect of resource competition between the two species. The frequency distribution of wet mass available to the two species differed significantly. This approach will enable a more robust construction of resource–consumer interaction networks. [ABSTRACT FROM AUTHOR]

How do cognitive pressures shape the lexicons of natural languages? Here, we reframe George Kingsley Zipf's proposed "law of abbreviation" within a more general framework that relates it to cognitive pressures that affect speakers and listeners. In this new framework, speakers' drive to reduce effort (Zipf's proposal) is counteracted by the need for low‐frequency words to have word forms that are sufficiently distinctive to allow for accurate recognition by listeners. To support this framework, we replicate and extend recent work using the prevalence of subword phonemic sequences (phonotactic probability) to measure speakers' production effort in place of Zipf's measure of length. Across languages and corpora, phonotactic probability is more strongly correlated with word frequency than word length. We also show this measure of ease of speech production (phonotactic probability) is strongly correlated with a measure of perceptual difficulty that indexes the degree of competition from alternative interpretations in word recognition. This is consistent with the claim that there must be trade‐offs between these two factors, and is inconsistent with a recent proposal that phonotactic probability facilitates both perception and production. To our knowledge, this is the first work to offer an explanation why long, phonotactically improbable word forms remain in the lexicons of natural languages. [ABSTRACT FROM AUTHOR]

This paper deals with statistical inference for lifetime data in presence of imperfect maintenance. For the maintenance model, the Sheu and Griffith model is considered. The lifetime distribution belongs to exponential distribution class. The maximum likelihood estimation procedure of the model parameters is discussed, and confidence intervals are provided using the asymptotic likelihood theory and bootstrap approach. Based on conjugate and discrete priors, Bayesian estimators of the model parameters are developed under symmetric and asymmetric loss functions. The proposed methodologies are applied to simulated data and sensitivity analysis to different parameters and data characteristics is carried out. The effect of model misspecification is also assessed within this class of distributions through a Monte Carlo simulation study. Finally, two datasets are analyzed for demonstrative aims. [ABSTRACT FROM AUTHOR]

The aim of this research was to assess genetic parameters for first lactation production and reproduction traits in Murrah buffaloes by employing additive and maternal effects. Data on pedigree and specific traits of 640 Murrah buffaloes were gathered from 1997 to 2020. These traits encompassed first lactation milk yield (FLMY), 305‐day first lactation milk yield (305FLMY), first lactation length (FLL), first lactation peak yield (FPY), first service period (FSP), first calving interval (FCI) and first dry period (FDP). Genetic evaluations employed six univariate animal models, accounting for both direct and maternal effects, facilitated by THRGIBBS1F90 and POSTGIBBSF90 programs. Fixed factors included in the analysis were period of calving, season of calving and age at first calving. The Bayesian estimates for direct heritability, derived from the most suitable model, were as follows: FLMY: 0.28 ± 0.01, 305FLMY: 0.30 ± 0.01, FLL: 0.19 ± 0.01, FPY: 0.18 ± 0.01, FSP: 0.12 ± 0.01, FCI: 0.14 ± 0.01 and FDP: 0.12 ± 0.01. Maternal effects were found significant, ranging from 5% to 10%, in first lactation traits under Model 2 and Model 5. Additionally, positive and significant genetic and phenotypic correlations were observed among the studied traits. In conclusion, selection based on 305‐day first lactation milk yield suggests potential for genetic enhancement in Murrah buffaloes, advocating its inclusion in breeding programmes to bolster early performance. Also, consideration of maternal influences is necessary for genetic progress of animals. [ABSTRACT FROM AUTHOR]

Brook trout (Salvelinus fontinalis) are a highly prized species found in lakes and streams throughout Ontario. A broadscale monitoring program (BsM) has been conducted in lakes throughout the province, in 5‐year cycles, which targets Salvelinus fontinalis populations. The objective of this study was to use the data gained from the BsM and establish a contemporary baseline on the variation of relative abundance and demographics of natural lake populations of S. fontinalis in Ontario. Additionally, given the variability in environmental conditions across the landscape, an objective was to assess the factors, abiotic and biotic, that influence relative abundance among waterbodies. Over the three BsM cycles (2008–2022), 273 S. fontinalis waterbodies were assessed, some multiple times, and 124 were unique populations. S. fontinalis were sampled in 112 of these unique lakes. In total, 7487 S. fontinalis were caught and 5372 were fully sampled (length, weight, and age assessed). The mean catch‐per‐unit‐effort (CPUE) in all waterbodies was 1.4 (0.12 standard error) S. fontinalis per net gang. There was not a significant trend in CPUE over the three BsM cycles nor did CPUE vary significantly among BsM cycles at the provincial scale, but CPUE varied significantly among fisheries management zones (FMZs). Multiple variables were examined to explain the variation in CPUE across the province; a negative association with species diversity was the only variable considered significant. Furthermore, S. fontinalis were more associated with the relative abundance of small cyprinids (i.e., chubs and shiners) and negatively associated with centrarchids, smelt, and other game species. Mean growth potential was greater for males than females but not significantly, whereas longevity favored females. Growth potential and parameters were similar between sexes within FMZs but varied among FMZs. Mean survival among these populations was 25.7%, ranging from 15.2% to 45.6%, with a mean instantaneous mortality of 1.41. This study provided a contemporary assessment of the current range and variability in relative abundance population dynamics of S. fontinalis in lakes within Ontario. This provides a benchmark to assess changes with the pending stressors of climate change and human expansion across the landscape. [ABSTRACT FROM AUTHOR]

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways. [ABSTRACT FROM AUTHOR]

The article proposes a new regression based on the generalized odd log-logistic family for interval-censored data. The survival times are not observed for this type of data, and the event of interest occurs at some random interval. This family can be used in interval modeling since it generalizes some popular lifetime distributions in addition to its ability to present various forms of the risk function. The estimation of the parameters is addressed by the classical and Bayesian methods. We examine the behavior of the estimates for some sample sizes and censorship percentages. Selection criteria, likelihood ratio tests, residual analysis, and graphical techniques assess the goodness of fit of the fitted models. The usefulness of the proposed models is red shown by means of two real data sets. [ABSTRACT FROM AUTHOR]

The replication crisis in empirical research calls for a more mindful approach to how we apply and report statistical models. For empirical research to have a lasting (policy) impact, these concerns are crucial. In this paper, we present Probabilistic Programming (PP) as a way forward. The PP workflow with an explicit data-generating process enhances the communication of model assumptions, code testing and consistency between theory and estimation. By simplifying Bayesian analysis, it also offers advantages for the interpretation, communication and modelling of uncertainty. We outline the advantages of PP to encourage its adoption in our community. [ABSTRACT FROM AUTHOR]

This article considers Bayesian model selection via mean-field (MF) variational approximation. Towards this goal, we study the non-asymptotic properties of MF inference that allows latent variables and model misspecification. Concretely, we show a Bernstein–von Mises (BvM) theorem for the variational distribution from MF under possible model misspecification, which implies the distributional convergence of MF variational approximation to a normal distribution centring at the maximal likelihood estimator. Motivated by the BvM theorem, we propose a model selection criterion using the evidence lower bound (ELBO), and demonstrate that the model selected by ELBO tends to asymptotically agree with the one selected by the commonly used Bayesian information criterion (BIC) as the sample size tends to infinity. Compared to BIC, ELBO tends to incur smaller approximation error to the log-marginal likelihood (a.k.a. model evidence) due to a better dimension dependence and full incorporation of the prior information. Moreover, we show the geometric convergence of the coordinate ascent variational inference algorithm, which provides a practical guidance on how many iterations one typically needs to run when approximating the ELBO. These findings demonstrate that variational inference is capable of providing a computationally efficient alternative to conventional approaches in tasks beyond obtaining point estimates. [ABSTRACT FROM AUTHOR]

Using compelling empirical strategies and sound reasoning, Simpson and Sims et al. ably contribute to our growing understanding of inferential errors that arise when filtering research findings using statistical significance. Drawing on the "Type M" (magnitude) and "Type S" (sign) errors described by Gelman and Carlin, both articles demonstrate that large Type M errors almost certainly exist when applying the statistical-significance filter in the field of education. In this comment, I discuss the pros and cons of four of the more actionable suggestions offered by the authors of both articles in their discussion sections. I also provide my own recommendations for drawing inferences about causal relationships that I believe will empower researchers and decision makers to more productively use evidence while avoiding the types of inferential mistakes examined by Sims et al. and Simpson. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to effectively measure the impact of a severe random shock, such as the COVID-19 pandemic on aggregate economic activity in Greece, seven other euro area economies, three Scandinavian countries, and the United States. The class of linear and quantile predictive regression models is proposed for the analysis of real gross domestic product (GDP) growth, and a Bayesian approach for model selection is developed, by using a computationally flexible Markov chain Monte Carlo stochastic search algorithm that explores the posterior distribution of linear and quantile models, and identifies the relevant predictor variables. Penalized likelihood regression models are also implemented to tackle the issue of model selection. The model confidence set approach is applied and verifies that the selected models identified by the stochastic search algorithm belong to the set of superior models. Our analysis confirms that the outbreak of the pandemic had a profound effect on the economies under study, and reveals that different predictor variables are able to explain different quantiles of the underlying real GDP growth distribution for analyzed countries, suggesting that the quantile modeling approach improves the ability to adequately explain real GDP series compared with the standard conditional mean approach that explains only the average of the relationship between real GDP growth and several predictor variables. [ABSTRACT FROM AUTHOR]

We examine the clustering behavior of price and variance jumps using high-frequency data, modeled as a marked Hawkes process (MHP) embedded in a bivariate jump-diffusion model with intraday periodic effects. We find that the jumps of both individual stocks and a broad index exhibit self-exciting behavior. The three dimensions of the model, namely positive price jumps, negative price jumps, and variance jumps, impact one another in an asymmetric fashion. We estimate model parameters using Bayesian inference by Markov Chain Monte Carlo, and find that the inclusion of the jump parameters improves the fit of the model. When we quantify the jump intensity and study the characteristics of jump clusters, we find that in high-frequency settings, jump clustering can last between 2.5 and 6 hours on average. We also find that the MHP generally outperforms other models in terms of reproducing two cluster-related characteristics found in the actual data. [ABSTRACT FROM AUTHOR]