Thermodynamic integration and steppingstone sampling methods for estimating Bayes factors: a tutorial

DOI10.1016/j.jmp.2019.01.005zbMath1431.62521OpenAlexW2913426552WikidataQ91662108 ScholiaQ91662108MaRDI QIDQ2332817

Jeffrey Annis, Nathan J. Evans, Thomas J. Palmeri, Brent J. Miller

Publication date: 5 November 2019

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6374050

zbMATH Keywords

Bayes factorssampling methodsMonte Carlo techniques

Mathematics Subject Classification ID

Bayesian inference (62F15) Monte Carlo methods (65C05) Applications of statistics to psychology (62P15)

Related Items

Hierarchical Bayesian mixture models of processing architectures and stopping rules ⋮ Improved marginal likelihood estimation via power posteriors and importance sampling ⋮ powder ⋮ jpnb4

Uses Software

• R
• BayesFactor
• tsbridge
• bridgesampling
• GitHub
• JASP

Cites Work

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• Default Bayes factors for ANOVA designs
• Empirical priors for reinforcement learning models
• An adaptive scheduling scheme for calculating Bayes factors with thermodynamic integration using Simpson's rule
• Prior sensitivity in theory testing: an apologia for the Bayes factor
• Constructing informative model priors using hierarchical methods
• A tutorial on Bayes factor estimation with the product space method
• Improving power posterior estimation of statistical evidence
• A Monte Carlo method for high dimensional integration
• Determining the dimensionality of multidimensional scaling representations for cognitive modeling
• A tutorial on Fisher information
• A tutorial on bridge sampling
• A Bayesian analysis of retention functions
• Bayesian model selection and model averaging
• The importance of complexity in model selection
• Model selection by normalized maximum likelihood
• Bayes factors: Prior sensitivity and model generalizability
• Marginal Likelihood from the Gibbs Output
• Handbook of Markov Chain Monte Carlo
• Marginal Likelihood Estimation via Power Posteriors
• Marginal Likelihood From the Metropolis–Hastings Output
• Bayes Factors
• Monte Carlo sampling methods using Markov chains and their applications
• Nested sampling for general Bayesian computation