A tutorial on bridge sampling.
Quentin Frederik Gronau,Alexandra Sarafoglou,Dora Matzke,Alexander Ly,Udo Boehm,Maarten Marsman,David S. Leslie,Jonathan J. Forster,Eric-Jan Wagenmakers,Helen Steingroever +9 more
TLDR
It is concluded that bridge sampling is an attractive method for mathematical psychologists who typically aim to approximate the marginal likelihood for a limited set of possibly high-dimensional models.About:
This article is published in Journal of Mathematical Psychology.The article was published on 2017-12-01 and is currently open access. It has received 196 citations till now. The article focuses on the topics: Marginal likelihood & Sampling (statistics).read more
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Crowdsourcing hypothesis tests: Making transparent how design choices shape research results
Justin F. Landy,Miaolei Liam Jia,Isabel L. Ding,Domenico Viganola,Warren Tierney,Anna Dreber,Magnus Johannesson,Thomas Pfeiffer,Charles R. Ebersole,Quentin Frederik Gronau,Alexander Ly,Don van den Bergh,Maarten Marsman,Koen Derks,Eric-Jan Wagenmakers,Andrew Proctor,Daniel M. Bartels,Christopher W. Bauman,William J. Brady,Felix Cheung,Andrei Cimpian,Simone Dohle,M. Brent Donnellan,Adam Hahn,Michael P. Hall,William Jiménez-Leal,David J. Johnson,Richard E. Lucas,Benoît Monin,Andres Montealegre,Elizabeth Mullen,Jun Pang,Jennifer L. Ray,Diego A. Reinero,Jesse Reynolds,Walter Sowden,Daniel Storage,Runkun Su,Christina M. Tworek,Jay J. Van Bavel,Daniel Walco,Julian Wills,Xiaobing Xu,Kai Chi Yam,Xiaoyu Yang,William A. Cunningham,Martin Schweinsberg,Molly Urwitz,Eric Luis Uhlmann +48 more
TL;DR: Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.
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Cognitive Bias Modification for Behavior Change in Alcohol and Smoking Addiction: Bayesian Meta-Analysis of Individual Participant Data.
Marilisa Boffo,Oulmann Zerhouni,Quentin Frederik Gronau,Ruben J. J. van Beek,Kyriaki Nikolaou,Maarten Marsman,Reinout W. Wiers +6 more
TL;DR: A Bayesian meta-analysis of individual patient data from studies investigating the effects of CBM as a behavior change intervention for the treatment of alcohol and tobacco use disorders, in individuals aware of the behavior change goal of the studies indicates the absence of enough evidence in favor or against a reliable effect ofCBM on cognitive bias and relapse rate in alcohol and Tobacco use disorders.
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How to become a Bayesian in eight easy steps: An annotated reading list.
TL;DR: This guide presents a reading list to serve as a concise introduction to Bayesian data analysis and provides commentary for eight recommended sources, which together cover the theoretical and practical cornerstones of Bayesian statistics in psychology and related sciences.
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Toward a principled Bayesian workflow in cognitive science.
TL;DR: A principled Bayesian workflow is introduced that provides guidelines and checks for valid data analysis, avoiding overfitting complex models to noise, and capturing relevant data structure in a probabilistic model.
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Reported self-control is not meaningfully associated with inhibition-related executive function: A Bayesian analysis
TL;DR: In this paper, the authors explored the association between self-reported self-control and two measures of inhibition-related executive functioning (the Stroop and Flanker paradigms).
References
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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.
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Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
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Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
TL;DR: In this article, the authors propose a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive.