Open AccessBook
Bayesian Cognitive Modeling: A Practical Course
TLDR
In this article, the basics of Bayesian analysis are discussed, and a WinBUGS-based approach is presented to get started with WinBUGs, which is based on the SIMPLE model of memory.Abstract:
Part I. Getting Started: 1. The basics of Bayesian analysis 2. Getting started with WinBUGS Part II. Parameter Estimation: 3. Inferences with binomials 4. Inferences with Gaussians 5. Some examples of data analysis 6. Latent mixture models Part III. Model Selection: 7. Bayesian model comparison 8. Comparing Gaussian means 9. Comparing binomial rates Part IV. Case Studies: 10. Memory retention 11. Signal detection theory 12. Psychophysical functions 13. Extrasensory perception 14. Multinomial processing trees 15. The SIMPLE model of memory 16. The BART model of risk taking 17. The GCM model of categorization 18. Heuristic decision-making 19. Number concept development.read more
Citations
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Journal ArticleDOI
Using Bayes to get the most out of non-significant results
TL;DR: It is argued Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches, and provides a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive.
Book
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
TL;DR: Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples.
Journal ArticleDOI
Bayesian inference for psychology. Part II: Example applications with JASP
Eric-Jan Wagenmakers,Jonathon Love,Maarten Marsman,Tahira Jamil,Alexander Ly,Josine Verhagen,Ravi Selker,Quentin Frederik Gronau,Damian Dropmann,Bruno Boutin,Frans Meerhoff,Patrick Knight,Akash Raj,Erik-Jan van Kesteren,Johnny van Doorn,Martin Šmíra,Sacha Epskamp,Alexander Etz,Dora Matzke,Tim de Jong,Don van den Bergh,Alexandra Sarafoglou,Helen Steingroever,Koen Derks,Jeffrey N. Rouder,Richard D. Morey +25 more
TL;DR: This part of this series introduces JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems.
Journal ArticleDOI
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications.
Eric-Jan Wagenmakers,Maarten Marsman,Tahira Jamil,Alexander Ly,Josine Verhagen,Jonathon Love,Ravi Selker,Quentin Frederik Gronau,Martin Šmíra,Sacha Epskamp,Dora Matzke,Jeffrey N. Rouder,Richard D. Morey +12 more
TL;DR: Ten prominent advantages of the Bayesian approach are outlined, and several objections to Bayesian hypothesis testing are countered.
Journal ArticleDOI
Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015
Colin F. Camerer,Anna Dreber,Felix Holzmeister,Teck-Hua Ho,Jürgen Huber,Magnus Johannesson,Michael Kirchler,Gideon Nave,Brian A. Nosek,Brian A. Nosek,Thomas Pfeiffer,Adam Altmejd,Nick Buttrick,Nick Buttrick,Taizan Chan,Yiling Chen,Eskil Forsell,Anup Gampa,Anup Gampa,Emma Heikensten,Lily Hummer,Taisuke Imai,Siri Isaksson,Dylan Manfredi,Julia Rose,Eric-Jan Wagenmakers,Hang Wu +26 more
TL;DR: It is found that peer beliefs of replicability are strongly related to replicable, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.
References
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Journal ArticleDOI
The measurement of observer agreement for categorical data
J. R. Landis,Gary G. Koch +1 more
TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
A Coefficient of agreement for nominal Scales
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.