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Background: In the digital environment, users' academic reading behaviour has changed, working with many articles simultaneously to search, filter, scan, link, annotate and analyse content fragments. The semantic enhancement environment has been widely set with semantic technologies to offer additional and handy support for users and thus facilitate the reading process. Despite many efforts devoted to developing a semantic enhancement environment, less attention has been paid to its actual effects. Objectives: This study aims to explore the effects of a semantic enhancement environment. Methods: Elaborating on cognitive load theory and focusing on academic reading, this study compared users' cognitive load and reading effect under the semantic enhancement environment and plain text environment and verified the three hypotheses. Results and conclusions: Through experiments and statistical analysis, this study found that, under a semantic enhancement environment, users had their cognitive load reduced and their reading effect improved. Consequently, a semantic enhancement environment promotes academic reading. Takeaways: This study adds to previous literature on semantic publishing by empirically corroborating the conducive effects of semantic enhancement environment in academic reading. Additionally, it justifies semantic publishing and provides a reference for the future design of semantically enhanced reading environments. Lay Description: What is already known about this topic?: The effects of a semantic enhancement environment are open to discuss.Academic reading is fundamental to researches and hence needs promotion.Limited studies investigated the effects of a semantic enhancement environment on academic reading.Publishers require effective reading environment designs. What this paper adds?: This study constructed a semantic enhancement reading environment to conduct a reading experiment.The experiment tested cognitive load, by using WP scale, and reading effects, by answer time and answer score.Semantic enhancement environment reduces users' cognitive load, promotes users' reading effects, and bolsters users' understanding of academic reading materials, especially in microstructure content.The design of semantic enhancement reading environments needs to fully consider the features of reading materials and give users a controllable way of interaction. The implications of study findings for practitioners: Compared to plain text environment, semantic enhancement environment is preferred in academic reading.Publishers are encouraged to add semantic enhancements to their publications.This study discussed how to design an effective learning and reading environment from the perspectives of reading resources, media forms of resources, and interaction modes. [ABSTRACT FROM AUTHOR]

In Wittgenstein's manuscripts dating from 1929 and 1930, there are a number of entries on the notion of probability. In this paper, I explore Wittgenstein's manuscripts between October 1929 and March 1930 and demonstrate, first, that Wittgenstein completely rejects the assumption that probability statements are based on an a priori principle. Second, I argue that the standard interpretation, which claims that Wittgenstein adopts an 'epistemological view' of probability, significantly understates the importance of Wittgenstein's development of the notion of hypotheses as a philosophical term of art. [ABSTRACT FROM AUTHOR]

Do freedom and social diversity affect individual preferences for revolutionary action? In this paper, we study the interplay between subjective freedom, defined as autonomy in decision‐making, and social diversity, measured through the extent of religious and/or ethno–linguistic fractionalization. Our paper is based on three hypotheses about the impact of the above two variables and their interaction on individual preferences for revolt. Our hypotheses are tested using a dataset containing information on about 44,000 individuals and covering 51 different countries during the 1990–2003 period. Our research suggests that people that define themselves as free individuals are less likely to support revolutionary actions, while the extent of fractionalization mildly affects such a probability. Interestingly, subjective freedom moderates the impact of diversity on the individual preferences for revolt if the extent of fractionalization is below a certain threshold. Instead, when above, subjective freedom enhances the impact of diversity on the taste for revolt. [ABSTRACT FROM AUTHOR]

An etymological proposition is often said to be probable or improbable from the phonetic point of view, and it is not rare for opinions to diverge on which it is. The estimation is typically purely intuitive, based on perceived similarity and no more than a handful of analogous examples. This paper proposes a method for quantifying the phonetic probability of an etymology and comparing it to the alternative hypothesis. It is intended to be used with sizable datasets, to produce a well-supported, objective verdict. [ABSTRACT FROM AUTHOR]

The paper offers new results about the probabilities of single-peaked preference profiles according to the impartial culture, impartial anonymous culture, impartial anonymous neutral culture, uniform culture, dual culture, and maximal culture assumptions. Two new probabilistic assumptions are studied. The uniform plurality culture assumption developed in the paper preserves uniformly distributed plurality votes, and it is easier than other culture assumptions. The case of abstention of voters is discussed. [ABSTRACT FROM AUTHOR]

Aiming at the imprecise and uncertain data and knowledge, this paper proposes a novel prior assumption by the rough set theory. The performance of the classical Bayesian classifier is improved through this study. We applied the operations of approximations to represent the imprecise knowledge accurately, and the concept of approximation quality is first applied in this method. Thus, this paper provides a novel rough set theory based prior probability in classical Bayesian classifier and the corresponding rough set prior Bayesian classifier. And we chose 18 public datasets to evaluate the performance of the proposed model compared with the classical Bayesian classifier and Bayesian classifier with Dirichlet prior assumption. Sufficient experimental results verified the effectiveness of the proposed method. The mainly impacts of our proposed method are: firstly, it provides a novel methodology which combines the rough set theory with the classical probability theory; secondly, it improves the accuracy of prior assumptions; thirdly, it provides an appropriate prior probability to the classical Bayesian classifier which can improve its performance only by improving the accuracy of prior assumption and without any effect to the likelihood probability; fourthly, the proposed method provides a novel and effective method to deal with the imprecise and uncertain data; last but not least, this methodology can be extended and applied to other concepts of classical probability theory, which providing a novel methodology to the probability theory. [ABSTRACT FROM AUTHOR]

This paper presents two new trajectory probability hypothesis density (TPHD) and trajectory cardinality probability hypothesis density (TCPHD) filters for joint tracking and classification (JTC), namely JTC-TPHD and JTC-TCPHD filters. We first introduce the classified trajectory RFS model to accommodate the motion model-based class information. The adaptation of the TPHD and TCPHD filters to the classified trajectory RFS is then formulated, i.e., JTC-TPHD and JTC-TCPHD, which are capable of jointly estimating the number, class, motion model, and trajectory status of multiple targets. We also develop the linear Gaussian implementation (LGM) as an analytic closed-form solution of the proposed filters. Simulation experiments discuss the validity and superiority of the proposed filters for multi-target JTC. • The classified trajectory variable to accommodate class information. • The proposal of JTC-T(C)PHD filters for joint tracking and classification. • The development of linear Gaussian mixture implementations. • The simulation demonstration of algorithms' validity and superiority. [ABSTRACT FROM AUTHOR]

This paper analyzes the historical context and systematic importance of Kant's hypothetical use of reason. It does so by investigating the role of hypotheses in Kant's philosophy of science. We first situate Kant's account of hypotheses in the context of eighteenth-century German philosophy of science, focusing on the works of Wolff, Meier, and Crusius. We contrast different conceptions of hypotheses of these authors and elucidate the different theories of probability informing them. We then adopt a more systematic perspective to discuss Kant's idea that scientific hypotheses must articulate real possibilities. We argue that Kant's views on the intelligibility of scientific hypotheses constitute a valuable perspective on scientific understanding and the constraints it imposes on scientific rationality. • Analyzes the role of hypotheses in eighteenth-century German philosophy. • Explains the relation between probability and hypotheses in various authors. • Explains how hypotheses paved the way for obtaining certain truths. • Explores the connection between hypotheses and scientific understanding in Kant. [ABSTRACT FROM AUTHOR]

In this paper, we define consistent criteria for hypotheses testing, such that the probability of any kind of error is zero for a given criterion. The necessary and sufficient conditions for the existence of such criteria are given. [ABSTRACT FROM AUTHOR]

• Knowledge mining on varying sets of compound hypotheses is inspected. • Bayesian learning is equipped with technique allowing to extend the set of hypotheses. • The technique copes with learning with less data than the amount of parameters. • The use of data forecasters as a knowledge source in parameter estimation is refined. • A rich set of interesting related research and application problems is presented. The paper proposes the way how to assign a proper prior probability to a new, generally compound, hypothesis. To this purpose, it uses the minimum relative-entropy principle and a forecaster-based knowledge transfer. Methodologically, it opens a way towards enriching the standard Bayesian framework by the possibility to extend the set of models during learning without the need to restart. The presented use scenarios concern: (a) creating new hypotheses, (b) learning problems with an insufficient amount of data, and (c) sequential Monte Carlo estimation. They indicate a strong application potential of the proposed technique. Related interesting open research problems are listed. [ABSTRACT FROM AUTHOR]

Highlights • The paper models a multi-player evolutionary game where players produce a collective good. • Modelling variants are considered where ollective goods may or may not be congestible, and may or may not be excludable. • For each variant, the effect a harsher environment on the probability that cooperation evolves is considered. • It is shown that a harsher environment fosters cooperation for charity goods (cooperators excluded from benefits), but hampers cooperation for club goods (defectors excluded from benefits). Abstract The common-enemy hypothesis of by-product mutualism proposes that organisms are more likely to cooperate when facing the common enemy of a harsher environment. Micro-foundations of this hypothesis have so far focused on the case where cooperation consists of the production of a pure public good. In this case, the effect of a harsher environment is ambiguous: not only a common-enemy effect is possible, but also an opposite, competing effect where the harsher environment reduces the probability of cooperation. This paper shows that unambiguous effects of a harsher environment are predicted when considering the realistic case where the collective good produced is excludable (in the sense that whether or not a player benefits from the collective good depends on whether or not he is contributing) and/or congestible (in the sense that the benefits the individual player obtains from the collective good are affected by the number of contributing players). In particular, the competing effect is systematically predicted for club goods, where defectors are excluded from the benefits of the collective good. A common-enemy effect is instead systematically predicted for charity goods, where cooperators are excluded from the benefits of the collective good. These effects are maintained for congestible club goods and for congestible charity goods. As the degree to which a collective good is excludable can be meaningfully compared across different instances of cooperation, these contrasting predictions for public good, charity goods and club goods yield testable hypotheses for the common-enemy hypothesis of by-product mutualism. [ABSTRACT FROM AUTHOR]

The most common objection to the Fine-Tuning Argument for the Multiverse is that the argument commits the Inverse Gambler's Fallacy. Simon Friederich has recently composed an interesting version of this fine-tuning argument that avoids this fallacy and better-matches important scientific instances of anthropic reasoning. My thesis in this paper is that this new argument, while it may avoid the fallacy, contains a disputable premise concerning the prior probabilities of the hypotheses at issue. I consider various ways to modify the argument to avoid this problem, but I argue that plausible replacements render other lines unjustified. I also briefly compare 'indexical' fine-tuning arguments such as Friederich's, according to which our universe permits life, to 'existential' fine-tuning arguments, according to which some universe or other permits life. I conclude that while Friederich is correct that the new fine-tuning argument avoids the Inverse Gambler's Fallacy, the argument nevertheless depends on an unjustified premise, and this is further reason for proponents of fine-tuning arguments for the multiverse to employ existential arguments rather than indexical arguments. [ABSTRACT FROM AUTHOR]

Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain experimental conditions have a statistically significant mean difference or there is a specific aspect between the group means. A problem occurs if the error rate increases while multiple hypothesis tests are performed simultaneously. Consequently, in an MCT, it is necessary to control the error rate to an appropriate level. In this paper, we discuss how to test multiple hypotheses simultaneously while limiting type I error rate, which is caused by a inflation. To choose the appropriate test, we must maintain the balance between statistical power and type I error rate. If the test is too conservative, a type I error is not likely to occur. However, concurrently, the test may have insufficient power resulted in increased probability of type II error occurrence. Most researchers may hope to find the best way of adjusting the type I error rate to discriminate the real differences between observed data without wasting too much statistical power. It is expected that this paper will help researchers understand the differences between MCTs and apply them appropriately. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the design of optimal relay and jammer selection strategy in relay-aided wireless networks. Different from previous works, assuming that the channel state information (CSI) of illegitimate nodes was available and only an eavesdropper existed, we first analyze disadvantages of joint relay and jammer selection (JRJS), average optimal relay selection (AORS), traditional maximum relay selection (TMRS) schemes. Then, we design an optimal relay and jammer selection strategy where the ratio of received SNRs at the destination generated by any two relays is maximized. By applying proposed strategy, computation complexity can be reduced. Moreover, we derive the lower and upper bounds of the secrecy outage probability based on the assumptions of existence of only illegitimate node and symmetric case for mathematical convenience. Finally, simulation shows that the proposed strategy operating with no CSI of illegitimate nodes can work efficiently compared with JRJS, TMRS and AORS strategies. • Optimal relay and jammer. • Transmission reliability. • Security performance. [ABSTRACT FROM AUTHOR]

This paper reflects on the importance that the dialectic of hypothesis - conjectures can have, not only for the development of demonstrative thinking in students, but also for the development of stochastic thinking. It argues for curricular reasons, for an approach to teaching based on problem solving, for a way of solving problems that considers simulation as a method of resolution with heuristic content and, finally, for new proposals on mathematics that the citizen of the XXI century will require and that includes the analysis of data in contexts of uncertainty. Consequently, a proposal is presented for the training of pre-service teachers to enable them to deal with such challenges. [ABSTRACT FROM AUTHOR]

Summary: Sample surveys are widely used to obtain information about totals, means, medians and other parameters of finite populations. In many applications, similar information is desired for subpopulations such as individuals in specific geographic areas and socio‐demographic groups. When the surveys are conducted at national or similarly high levels, a probability sampling can result in just a few sampling units from many unplanned subpopulations at the design stage. Cost considerations may also lead to low sample sizes from individual small areas. Estimating the parameters of these subpopulations with satisfactory precision and evaluating their accuracy are serious challenges for statisticians. To overcome the difficulties, statisticians resort to pooling information across the small areas via suitable model assumptions, administrative archives and census data. In this paper, we develop an array of small area quantile estimators. The novelty is the introduction of a semiparametric density ratio model for the error distribution in the unit‐level nested error regression model. In contrast, the existing methods are usually most effective when the response values are jointly normal. We also propose a resampling procedure for estimating the mean square errors of these estimators. Simulation results indicate that the new methods have superior performance when the population distributions are skewed and remain competitive otherwise. [ABSTRACT FROM AUTHOR]

Summary: Computerised Record Linkage methods help us combine multiple data sets from different sources when a single data set with all necessary information is unavailable or when data collection on additional variables is time consuming and extremely costly. Linkage errors are inevitable in the linked data set because of the unavailability of error‐free unique identifiers. A small amount of linkage errors can lead to substantial bias and increased variability in estimating parameters of a statistical model. In this paper, we propose a unified theory for statistical analysis with linked data. Our proposed method, unlike the ones available for secondary data analysis of linked data, exploits record linkage process data as an alternative to taking a costly sample to evaluate error rates from the record linkage procedure. A jackknife method is introduced to estimate bias, covariance matrix and mean squared error of our proposed estimators. Simulation results are presented to evaluate the performance of the proposed estimators that account for linkage errors. [ABSTRACT FROM AUTHOR]

It is known that operator splitting methods based on forward–backward splitting, Douglas–Rachford splitting, and Davis–Yin splitting decompose difficult optimization problems into simpler subproblems under proper convexity and smoothness assumptions. In this paper, we identify an envelope (an objective function), whose gradient descent iteration under a variable metric coincides with Davis–Yin splitting iteration. This result generalizes the Moreau envelope for proximal-point iteration and the envelopes for forward–backward splitting and Douglas–Rachford splitting iterations identified by Patrinos, Stella, and Themelis. Based on the new envelope and the stable–center manifold theorem, we further show that, when forward–backward splitting or Douglas–Rachford splitting iterations start from random points, they avoid all strict saddle points with probability one. This result extends the similar results by Lee et al. from gradient descent to splitting methods. [ABSTRACT FROM AUTHOR]

A new Bayesian approach for multiple satellite faults detection and exclusion is proposed by introducing a classification variable to each satellite observation. If we treat this classification variable as random and assume a prior distribution for it, then a rule for satellite fault detection and exclusion based on the posterior probabilities of the classification variables is constructed under the framework of Bayesian hypothesis testing. Secondly, the Gibbs sampler is introduced to compute the posterior probabilities of the classification variables. Then the implementation for a Bayesian Receiver Autonomous Integrity Monitoring (RAIM) algorithm is designed with the Gibbs sampler. Finally, different schemes are designed to evaluate the performance of the new Bayesian RAIM algorithm in the case of multiple faults. We compare the method in this paper with the Range Consensus (RANCO) method. Experiments illustrate that the proposed algorithm in this paper is capable of detecting and eliminating multiple satellite faults, and the probability of correctly detecting faults is high. [ABSTRACT FROM AUTHOR]

A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This “multivariable Mendelian randomization” approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol. [ABSTRACT FROM PUBLISHER]

Traffic surveillance is an important issue in intelligent transportation systems. Efficient and accurate vehicle detection is one challenging problem for complex urban traffic surveillance. As such, this paper proposes a new vehicle detection method using spatial relationship GMM for daytime and nighttime based on a high-resolution camera. First, the vehicle is treated as an object composed of multiple components, including the license plate, rear lamps and headlights. These components are localized using their distinctive color, texture, and region feature. Deriving plate color converting model, plate hypothesis score calculation and cascade plate refining were accomplished for license plate localization. Multi-threshold segmentation and connected component analysis are accomplished for rear lamps localization. Frame difference and geometric features similarity analysis are accomplished for headlights localization. After that, the detected components are taken to construct the spatial relationship using GMM. Finally, similar probability measures of the model and the GMM, including GMM of plate and rear lamp, GMM of both rear lamps and GMM of both headlights are adopted to localize vehicle. Experiments in practical urban scenarios are carried out under daytime and nighttime. It can be shown that our method can adapt to the partial occlusion and various lighting conditions well, meanwhile it has a fast detection speed. [ABSTRACT FROM AUTHOR]

Abstract In this note, we study quasi-ergodicity for one-dimensional diffusions on ( 0 , ∞ ) , where 0 is an exit boundary and +∞ is an entrance boundary. Our main aim is to improve some results obtained by He and Zhang (2016) [3]. In simple terms, the same main results of the above paper are obtained with more relaxed conditions. Résumé Nous étudions la quasi-ergodicité des diffusions unidimensionnelles sur ] 0 , ∞ [ , où 0 est une frontière de sortie et ∞ une frontière d'entrée. Notre but est d'améliorer des résultats obtenus par He and Zhang (2016) [3]. Ainsi, nous retrouvons les résultats principaux de ce texte sous des hypothèses moins restrictives. [ABSTRACT FROM AUTHOR]

A measurement-driven multi-target multi-Bernoulli (MeMBer) filter which modifies the MeMBer filter by the measurements information is proposed in this paper. The proposed filter refines both the legacy estimates and the data-induced estimates of the MeMBer filter. For the targets under the legacy track set, the detection probabilities derived from the measurements are employed to refine the multi-target distribution. And for the targets under the data-induced track set, the multi-target distribution is further improved by the modified existence probabilities of the legacy tracks. Unlike the cardinality balanced MeMBer (CBMeMBer) filter, the proposed filter removes the cardinality bias in the MeMBer filter by utilizing the measurements information. Simulation results show that, compared with the traditional methods, the proposed filter can improve the stability and accuracy of the estimates and does not need the high detection probability hypothesis. [ABSTRACT FROM AUTHOR]

Reordering is one of the most important factors affecting the quality of the output in statistical machine translation (SMT). A considerable number of approaches that proposed addressing the reordering problem are discriminative reordering models (DRM). The core component of the DRMs is a classifier which tries to predict the correct word order of the sentence. Unfortunately, the relationship between classification quality and ultimate SMT performance has not been investigated to date. Understanding this relationship will allow researchers to select the classifier that results in the best possible MT quality. It might be assumed that there is a monotonic relationship between classification quality and SMT performance, i.e., any improvement in classification performance will be monotonically reflected in overall SMT quality. In this paper, we experimentally show that this assumption does not always hold, i.e., an improvement in classification performance might actually degrade the quality of an SMT system, from the point of view of MT automatic evaluation metrics. However, we show that if the improvement in the classification performance is high enough, we can expect the SMT quality to improve as well. In addition to this, we show that there is a negative relationship between classification accuracy and SMT performance in imbalanced parallel corpora. For these types of corpora, we provide evidence that, for the evaluation of the classifier, macro-averaged metrics such as macro-averaged F-measure are better suited than accuracy, the metric commonly used to date. [ABSTRACT FROM AUTHOR]

It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. As a result, the classification algorithms often have poor learning performances due to slow convergence in the smaller classes. To balance such data sets, this paper presents a strategy that involves reducing the sizes of the majority data and generating synthetic samples for the minority data. In the reducing operation, we use the box-and-whisker plot approach to exclude outliers and the Mega-Trend-Diffusion method to find representative data from the majority data. To generate the synthetic samples, we propose a counterintuitive hypothesis to find the distributed shape of the minority data, and then produce samples according to this distribution. Four real datasets were used to examine the performance of the proposed approach. We used paired t-tests to compare the Accuracy, G-mean, and F-measure scores of the proposed data pre-processing (PPDP) method merging in the D3C method (PPDP+D3C) with those of the one-sided selection (OSS), the well-known SMOTEBoost (SB) study, and the normal distribution-based oversampling (NDO) approach, and the proposed data pre-processing (PPDP) method. The results indicate that the classification performance of the proposed approach is better than that of above-mentioned methods. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the stress management skills of Christian Orthodox monks and nuns, as measured by Antonovsky's scale sense of coherence (SOC). A case-control study was designed to test the hypotheses whether monks and nuns have higher SOC than secular people. The study population consisted of 193 individuals, 96 monks and nuns (study group) and 97 secular men and women (control group). SOC score was higher in monks and nuns as compared to the secular population ( p = 0.002), men as compared to women ( p = 0.012) and persons of older age ( p = 0.004) as compared to younger individuals. [ABSTRACT FROM AUTHOR]

We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64–1.46) for nominally statistically significant results and D = 0.24 (0.11–0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience. [ABSTRACT FROM AUTHOR]

Background and Aims It has been proposed that more use should be made of Bayes factors in hypothesis testing in addiction research. Bayes factors are the ratios of the likelihood of a specified hypothesis (e.g. an intervention effect within a given range) to another hypothesis (e.g. no effect). They are particularly important for differentiating lack of strong evidence for an effect and evidence for lack of an effect. This paper reviewed randomized trials reported in Addiction between January and June 2013 to assess how far Bayes factors might improve the interpretation of the data. Methods Seventy-five effect sizes and their standard errors were extracted from 12 trials. Seventy-three per cent ( n = 55) of these were non-significant (i.e. P > 0.05). For each non-significant finding a Bayes factor was calculated using a population effect derived from previous research. In sensitivity analyses, a further two Bayes factors were calculated assuming clinically meaningful and plausible ranges around this population effect. Results Twenty per cent ( n = 11) of the non-significant Bayes factors were < ⅓ and 3.6% ( n = 2) were > 3. The other 76.4% ( n = 42) of Bayes factors were between ⅓ and 3. Of these, 26 were in the direction of there being an effect (Bayes factor > 1 and < 3); 12 tended to favour the hypothesis of no effect (Bayes factor < 1 and > ⅓); and for four there was no evidence either way (Bayes factor = 1). In sensitivity analyses, 13.3% of Bayes Factors were < ⅓ ( n = 20), 62.7% ( n = 94) were between ⅓ and 3 and 24.0% ( n = 36) were > 3, showing good concordance with the main results. Conclusions Use of Bayes factors when analysing data from randomized trials of interventions in addiction research can provide important information that would lead to more precise conclusions than are obtained typically using currently prevailing methods. [ABSTRACT FROM AUTHOR]

Scientific discovery is about a search for the Truth, for the consistent and predictable in how the universe works. Using a particular method of inquiry, the scientific method, and with acknowledgement of the inherently self-correcting nature of science, scientific inquiry moves forward incrementally to ever closer approximations of the Truth. This paper reviews the history of scientific inquiry, the methodology of the scientific method, including the necessity for hypothesis testing and development of the probability that a particular answer is a closer approximation of the Truth than previous answers have been. It also discusses some of the pitfalls of scientific inquiry, and areas in which the search for Truth may be corrupted. [ABSTRACT FROM AUTHOR]

Background and Aims If socio-economic disadvantage is associated with more adolescent smoking, but less participation in tertiary education, and smoking and tertiary education are both associated with heavier drinking, these may represent opposing pathways to heavy drinking. This paper examines contextual variation in the magnitude and direction of these associations. Design Comparing cohort studies. Setting United Kingdom. Participants Participants were from the 1958 National Child Development Study (NCDS58; n = 15 672), the British birth cohort study (BCS70; n = 12 735) and the West of Scotland Twenty-07 1970s cohort (T07; n = 1515). Measurements Participants self-reported daily smoking and weekly drinking in adolescence (age 16 years) and heavy drinking (> 14/21 units in past week) in early adulthood (ages 22-26 years). Parental occupational class (manual versus non-manual) indicated socio-economic background. Education beyond age 18 was coded as tertiary. Models were adjusted for parental smoking and drinking, family structure and adolescent psychiatric distress. Findings Respondents from a manual class were more likely to smoke and less likely to enter tertiary education (e.g. in NCDS58, probit coefficients were 0.201 and -0.765, respectively; P < 0.001 for both) than respondents from a non-manual class. Adolescent smokers were more likely to drink weekly in adolescence (0.346; P < 0.001) and more likely to drink heavily in early adulthood (0.178; P < 0.001) than adolescent non-smokers. Respondents who participated in tertiary education were more likely to drink heavily in early adulthood (0.110 for males, 0.182 for females; P < 0.001 for both) than respondents with no tertiary education. With some variation in magnitude, these associations were consistent across all three cohorts. Conclusions In Britain, young adults are more likely to drink heavily both if they smoke and participate in tertiary education (college and university) despite socio-economic background being associated in opposite directions with these risk factors. [ABSTRACT FROM AUTHOR]

Preferential sampling refers to any situation in which the spatial process and the sampling locations are not stochastically independent. In this paper, we present two examples of geostatistical analysis in which the usual assumption of stochastic independence between the point process and the measurement process is violated. To account for preferential sampling, we specify a flexible and general Bayesian geostatistical model that includes a shared spatial random component. We apply the proposed model to two different case studies that allow us to highlight three different modeling and inferential aspects of geostatistical modeling under preferential sampling: (1) continuous or finite spatial sampling frame; (2) underlying causal model and relevant covariates; and (3) inferential goals related to mean prediction surface or prediction uncertainty. [ABSTRACT FROM AUTHOR]

This paper takes a mathematical analysis technique derived from the Interrogator's Fallacy (in a legal context), expands upon it to identify a set of three interrelated probabilistic tools with wide applicability, and demonstrates their ability to assess hypothesis-evidence relationships associated with important problems. [ABSTRACT FROM AUTHOR]

Self-efficacy is arguably the strongest correlate of physical activity, yet some researchers suggest this is because the construct confounds ability with motivation. We examine a more circumscribed construct, called perceived capability (PC), meant to measure ability but not motivation and propose that the construct will not be related to unskilled physical activities but may be linked to skilled behaviors. The purpose of this paper was to examine whether a PC construct can be stripped of motivation using a vignette approach in both walking and resistance training behaviors. Participants were a random sample of 248 university students, who were then randomly assigned to either answer resistance training or walking behavior questions. Both groups completed a PC measure and reasons for their answer before and after reading a vignette that clarified the phrasing of capability to a literal use of the term. PC was significantly (p < .01) higher post- compared to pre-vignette and the differences were greater (p < .01) for walking than for resistance training. PC had significantly (p < .01) smaller correlations with intention and self-reported behavior post-disambiguation, which resulted in a null relationship with walking but a small correlation with resistance training behavior. When PC was combined with intention to predict behavior, however, there was no significant (p > .05) difference in the amount of variance explained pre- to post-vignette. Thought listing showed that participants did not report capability barriers to walking and over half of the sample construed capability as motivation/other priorities pre-vignette. The findings support use of a vignette approach for researchers who wish to disentangle the assessment of PC from motivation while creating no overall loss in explained variance of physical activity. [ABSTRACT FROM AUTHOR]

The current paper introduces the notion of clinically relevant subtypes of emotion regulation behaviours. A new measure of emotion regulation, the Complexity of Emotional Regulation Scale (CERS), was established as psychometrically sound. It was positively correlated with a measure of emotional awareness ( r = 0.28, p < 0.001) and negatively correlated with measures of self-criticism ( r = −0.28, p < 0.001) and depression ( r = −0.35, p = 0.025), among others. Participants were drawn from two samples: clients from a university counselling centre and a non-clinical student sample. Comparisons were conducted between non-clinical and clinical samples to determine the effects of depression and other symptoms of psychopathology on participant's generation of strategies for emotion regulation. Participants in the clinical sample more often identified an intention to soothe but did not follow through as compared with the non-clinical group, F(1, 198) = 4.662, p < 0.04. Furthermore, individuals in the non-clinical sample were more likely to engage in specific, meaning-making strategies when compared with the clinical group, F(1, 198) = 5.875, p < 0.02. Implications from the current studies suggest the possible applicability of the CERS to clinical settings using an interview rather than questionnaire format. Copyright © 2015 John Wiley & Sons, Ltd. Key Practitioner Message: Emotion regulation should be thought of as being on a continuum of complexity, where strategies range from general ('one size fits all') action to specific ('personal and idiosyncratic') meaning. The best emotion regulation strategy depends on a client's presenting difficulty and level of distress. [ABSTRACT FROM AUTHOR]

In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches select the transition density as the importance sampling (IS) function, which is inefficient in a nonlinear scenario. To enhance the performance of the conventional SMC-PHD filter, we propose in this paper two approaches using the cubature information filter (CIF) for multi-target tracking. More specifically, we first apply the posterior intensity as the IS function. Then, we propose to utilize the CIF algorithm with a gating method to calculate the IS function, namely CISMC-PHD approach. Meanwhile, a fast implementation of the CISMC-PHD approach is proposed, which clusters the particles into several groups according to the Gaussian mixture components. With the constructed components, the IS function is approximated instead of particles. As a result, the computational complexity of the CISMC-PHD approach can be significantly reduced. The simulation results demonstrate the effectiveness of our approaches. [ABSTRACT FROM AUTHOR]

Misinformation encountered after witnessing an event is known to influence subsequent memory reports about this event. In most research, misleading information was introduced impersonally, for example, by means of a written description, but it is now well established that delivering it in a social interaction is effective as well. Less is known about the relative effectiveness of impersonal post-event misinformation compared with a socially presented one. The present research provides a direct empirical comparison between social, para-social, and impersonal methods of delivering misinformation. Results indicate that the way in which post-event information is provided does not affect the number of false recall items, source monitoring, or remember-know distinction, with a high Bayesian probability of the obtained no-difference effects. Results show that the social conformity factor does not significantly influence the impact of misleading post-event information. The paper also provides a theoretical comparison of the two effects. [ABSTRACT FROM AUTHOR]

Dozens of studies have shown that authoritarian people are ethnocentric. They are described as nationalistic, prejudiced, and hostile toward ethnic/national out-groups. However, it can be argued that this critical claim remains unproven, as researchers do not take into consideration the very conservative right-wing views typical of authoritarian people. To address this problem, two theoretical approaches were contrasted: the most commonly used right-wing authoritarianism (RWA) approach and the group authoritarianism approach. Both approaches define authoritarianism as the covariance of submission, aggression, and conventionalism, but they differ in that the former is inextricably related to right-wing ideology while the latter is not. This paper presents the results of two survey studies conducted on samples of 600 and 400 respondents. In Study 1, it was found that RWA and group authoritarianism had different patterns of relationships with in-group and out-group national attitudes, providing strong evidence in support of the hypothesis that the right-wing ideology generated ethnocentric national attitudes. Study 2 showed a strong direct effect of right-wing ideology on ethnocentric national attitudes, while the impact of 'pure' authoritarian covariance is marginal and sometimes even seems to be negative (e.g., ethnic prejudices). These findings suggest that authoritarianism has little to do with ethnocentric national attitudes. It is not the covariance of authoritarian attitudes that results in growing ethnocentrism. The true perpetrator can be found in the large component of right-wing ideology contained in such measurement instruments as the RWA scale. [ABSTRACT FROM AUTHOR]

In risk assessment we are typically faced with a huge number of potential scenarios and events, and in practise some of these are ignored, either because they are not identified or because of judged low probability. However, a scenario or an event may occur despite being extremely unlikely. Considering a large population of such scenarios and events, the occurrence probability is not necessarily negligible. In this paper we take a closer look at this challenge, the main aim being to clarify the issue and provide some recommendation on how to best handle it in practise. A main conclusion is that the risk assessment should be placed in a sufficiently broad framework, ensuring that the outcome and main event spaces are complete, and sufficient focus is placed on the hypotheses and assumptions supporting the detailed scenarios that are identified. [ABSTRACT FROM AUTHOR]

When researchers are interested in the effect of certain interventions on certain individuals, single-subject studies are often performed. In their most simple form, such single-subject studies require that a subject is measured on relevant criterion variables several times before an intervention and several times during or after the intervention. Scores from the two phases are then compared in order to investigate the intervention effect. Since observed scores typically consist of a mixture of true scores and random measurement error, simply looking at the difference in scores can be misleading. Hence, de Vries & Morey (2013) developed models and hypothesis tests for single-subject data, quantifying the evidence in data for the size and presence of an intervention effect. In this paper we give a non-technical overview of the models and hypothesis tests and show how they can be applied on real data using the BayesSingleSub R package, with the aid of an empirical data set. [ABSTRACT FROM AUTHOR]

There is an ongoing controversy in philosophy about the connection between explanation and inference. According to Bayesians, explanatory considerations should be given weight in determining which inferences to make, if at all, only insofar as doing so is compatible with Strict Conditionalization. Explanationists, on the other hand, hold that explanatory considerations can be relevant to the question of how much confidence to invest in our hypotheses in ways which violate Strict Conditionalization. The controversy has focused on normative issues. This paper investigates experimentally the descriptive question of whether judgments of the explanatory goodness of hypotheses do play a role when people revise their degrees of belief in those hypotheses upon the receipt of new evidence. We present the results of three experiments that together strongly support the predictive superiority of the explanationist position. [ABSTRACT FROM AUTHOR]

Robert Aumann presents his Agreement Theorem as the key conditional: “if two people have the same priors and their posteriors for an event A are common knowledge, then these posteriors are equal” (Aumann, 1976, p. 1236). This paper focuses on four assumptions which are used in Aumann’s proof but are not explicit in the key conditional: (1) that agents commonly know, of some prior μ, that it is the common prior; (2) that agents commonly know that each of them updates on the prior by conditionalization; (3) that agents commonly know that if an agent knows a proposition, she knows that she knows that proposition (the “KK” principle); (4) that agents commonly know that they each update only on true propositions. It is shown that natural weakenings of any one of these strong assumptions can lead to countermodels to Aumann’s key conditional. Examples are given in which agents who have a common prior and commonly know what probability they each assign to a proposition nevertheless assign that proposition unequal probabilities. To alter Aumann’s famous slogan: people can “agree to disagree”, even if they share a common prior. The epistemological significance of these examples is presented in terms of their role in a defense of the Uniqueness Thesis: If an agent whose total evidence is E is fully rational in taking doxastic attitude D to P, then necessarily, any subject with total evidence E who takes a different attitude to P is less than fully rational. [ABSTRACT FROM AUTHOR]