Bayesian data analysis for newcomers

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.     

What people believe about memory

Two representative samples of adult Norwegians (n=2000) were asked a set of general and specific questions regarding their beliefs and opinions about human memory. The results indicate that on many questions, such as time of the earliest memories, inhibiting effects of collaboration, and memory for dramatic versus ordinary events, the views of the general public concurred with current research findings, and people in general had realistic views about their own memory performance. On other questions, such as the reliability of olfactory as compared with visual and auditory memory, the memory of small children in comparison with that of adults, the likelihood of repression of adult traumatic memories, and on more general questions such as the possibility of training memory and the capacity limitations of long-term memory, a large proportion of the participants expressed views that are less supported by scientific evidence. Implications of these findings are briefly discussed.     

What people believe about memory

Two representative samples of adult Norwegians (n=2000) were asked a set of general and specific questions regarding their beliefs and opinions about human memory. The results indicate that on many questions, such as time of the earliest memories, inhibiting effects of collaboration, and memory for dramatic versus ordinary events, the views of the general public concurred with current research findings, and people in general had realistic views about their own memory performance. On other questions, such as the reliability of olfactory as compared with visual and auditory memory, the memory of small children in comparison with that of adults, the likelihood of repression of adult traumatic memories, and on more general questions such as the possibility of training memory and the capacity limitations of long-term memory, a large proportion of the participants expressed views that are less supported by scientific evidence. Implications of these findings are briefly discussed.     

What members of Alcoholics Anonymous really believe

Many mental health professionals work with people who are members of Alcoholics Anonymous (AA) or may benefit from AA attendance. This article provides practical information about AA that professionals can use to understand and work with people in AA. It is a summary of a survey of 187 members of AA as well as synthesising the literature on AA. Specifically, it describes what AA is, the twelve steps and principles that underlie them, common AA expressions and how they can be used in therapy, common concerns about AA, and how to get more information about AA. Implications of how AA's steps and language can be incorporated into therapy, how therapy can facilitate working the steps of AA, and how to address consumers’ (or professionals’) concerns about AA are addressed.     

Bayesian analysis of order-statistics models for ranking data

In this paper, a class of probability models for ranking data, the order-statistics models, is investigated. We extend the usual normal order-statistics model into one where the underlying random variables follow a multivariate normal distribution. Bayesian approach and the Gibbs sampling technique are used for parameter estimation. In addition, methods to assess the adequacy of model fit are introduced. Robustness of the model is studied by considering a multivariate-t distribution. The proposed method is applied to analyze the presidential election data of the American Psychological Association (APA).The author is grateful to K. Lam, K.F. Lam, the Editor, an associate editor, and three reviewers for their valuable comments and suggestions. This research was substantially supported by the CRCG grant 335/017/0015 of the University of Hong Kong and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7169/98H). Upon completion of this paper, I became aware that similar work had been done independently by K.G. Yao and U. Böckenholt (1999).     

Bayesian data analysis as a tool for behavior analysts

Bayesian approaches to data analysis are considered within the context of behavior analysis. The paper distinguishes between Bayesian inference, the use of Bayes Factors, and Bayesian data analysis using specialized tools. Given the importance of prior beliefs to these approaches, the review addresses those situations in which priors have a big effect on the outcome (Bayes Factors) versus a smaller effect (parameter estimation). Although there are many advantages to Bayesian data analysis from a philosophical perspective, in many cases a behavior analyst can be reasonably well‐served by the adoption of traditional statistical tools as long as the focus is on parameter estimation and model comparison, not null hypothesis significance testing. A strong case for Bayesian analysis exists under specific conditions: When prior beliefs can help narrow parameter estimates (an especially important issue given the small sample sizes common in behavior analysis) and when an analysis cannot easily be conducted using traditional approaches (e.g., repeated measures censored regression).     

What US judges know and believe about eyewitness testimony

In a survey, 160 US judges indicated their knowledge and beliefs about eyewitness testimony. Although correct on some issues, judges were often wrong on important issues such as whether at trial eyewitness confidence is a good indicator of eyewitness accuracy, and if jurors can distinguish accurate from inaccurate witnesses. Increased knowledge was associated with: a willingness to permit legal safeguards, including expert testimony at trial; a belief that jurors have limited knowledge of eyewitness factors; a reluctance to convict defendants solely from eyewitness testimony; a more accurate estimate of the extent to which wrongful convictions result from eyewitness error; and a belief that judges need more eyewitness training. Additional training about factors and procedures that affect eyewitness accuracy may help judges reduce the number of wrongful convictions. Copyright © 2004 John Wiley & Sons, Ltd.     

Choosing to believe

This article defends a regulative ethics of voluntary belief. In order to determine the occasion and the scope of such an ethics, the article begins with an examination of the concept of belief in conversation with the view of J. L. Schellenberg. Next, against the dominant position in contemporary epistemology, it argues that some beliefs can be voluntary, in the sense that they are under the immediate control of the believer, and replies to William Alston’s influential objections to doxastic voluntarism. If some beliefs are subject to the immediate control of the believer, then in these cases believers are ethically responsible not only for how they investigate those beliefs, but also for the choice of whether or not to believe them. The article concludes by formulating and defending two types of regulative ethical principles governing voluntary belief.     

Seemings: still dispositions to believe

According to phenomenal conservatism, seemings can provide prima facie justification for beliefs. In order to fully assess phenomenal conservatism, it is important to understand the nature of seemings. Two views are that (SG) seemings are a sui generis propositional attitude, and that (D2B) seemings are nothing over and above dispositions to believe. Proponents of (SG) reject (D2B) in large part by providing four distinct objections against (D2B). First, seemings have a distinctive phenomenology, but dispositions to believe do not. Second, seemings can provide a non-trivial explanation for dispositions to believe, which wouldn’t be possible if seemings were dispositions to believe. Third, there are some dispositions to believe that are not seemings. Fourth, there are instances of seemings which are not dispositions to believe. I consider and reject each of these objections. The first and third objections rely on a misunderstanding of (D2B). The second objection fails because there are contexts in which an appeal to a previously unknown identity can provide an interesting explanation. The fourth objection overlooks the possibility of finkish and masked dispositions, phenomena which are widely accepted in the dispositions literature. I conclude that (D2B) escapes these common objections unscathed.     

因"用"而信与因"信"而信--以清末中国士大夫宗教观为例的研究

在宗教学上 ,因“用”而信是指信者认为其有用而信 ;因“信”而信是指信者认为其真实而信。从清末中国士大夫的宗教观的流变看 ,单纯强调宗教是否有用的问题 ,而忽视其是否为真的问题 ,必然会影响宗教信仰的坚定性。     

Bayesian latent variable models for the analysis of experimental psychology data

In this paper, we address the use of Bayesian factor analysis and structural equation models to draw inferences from experimental psychology data. While such application is non-standard, the models are generally useful for the unified analysis of multivariate data that stem from, e.g., subjects’ responses to multiple experimental stimuli. We first review the models and the parameter identification issues inherent in the models. We then provide details on model estimation via JAGS and on Bayes factor estimation. Finally, we use the models to re-analyze experimental data on risky choice, comparing the approach to simpler, alternative methods.     

Intuitions are inclinations to believe

Advocates of the use of intuitions in philosophy argue that they are treated as evidence because they are evidential. Their opponents agree that they are treated as evidence, but argue that they should not be so used, since they are the wrong kinds of things. In contrast to both, we argue that, despite appearances, intuitions are not treated as evidence in philosophy whether or not they should be. Our positive account is that intuitions are a subclass of inclinations to believe. Our thesis explains why intuitions play a role in persuasion and inquiry, without conceding that they are evidential. The account also makes predictions about the structure of intuitions that are confirmed by independent arguments.