Chapter 16 Bayesian Methods
16.0.1 Theory and methods
Bayesian statistics: Wikipedia page
Andrew Gelman et al., Bayesian Data Analysis (third edition) (Andrew Gelman and Rubin 2014).
Robert Grant (2018-08-07) How (not) to introduce newcomers to Bayesian analysis
16.0.1.1 R-based
Richard McElreath, 2016, Statistical Rethinking: A Bayesian Course with Examples in R and Stan (McElreath 2016).
{rethinking}, the companion R package
McElreath’s YouTube channel, with Statistical Rethinking lectures
Jim Albert, Bayesian Computation with R (Albert 2009)
David Robinson, Introduction to Empirical Bayes
book announcement (2017-12-27)
based on 10 blog posts: the final post has a listing of the previous 9 “Simulation of empirical Bayesian methods (using baseball statistics)”
Daniel Lüdecke, 2018-06-06, R functions for Bayesian Model Statistics and Summaries
Rasmus Bååth (2019-07-15) Get up to speed with Bayesian data analysis in R, from UseR2019
16.0.1.2 other
Tarek Amr, “Experimenting the Bayesian way”; summary of Bayesian approach with Python examples (2018-07-18)
16.0.2 R
Arranged by package
16.0.2.1 {bayestestR}
package
GitHub page: bayestestR: Utilities for analyzing Bayesian models and posterior distributions
articles
easystats (2019-04-15) Describe and understand Bayesian models and posteriors using bayestestR
16.0.2.2 {HydeNet}
CRAN: HydeNet: Hybrid Bayesian Networks Using R and JAGS
Vignette: Decision Network (Influence Diagram) Analyses in HydeNet
16.0.2.3 {rjags}
package
CRAN page: rjags: Bayesian Graphical Models using MCMC
articles
Alicia Johnson: [Bayesian modeling with {rjags}] {link to DataCamp course removed}
16.0.2.4 {tidybayes}
package
CRAN page: tidybayes: Tidy Data and ‘Geoms’ for Bayesian Models
GitHub page: Bayesian analysis + tidy data + geoms (R package)
articles
Matthew Kay, tidybayes: Bayesian analysis + tidy data + geoms
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