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51_bayesian.Rmd
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# Bayesian Methods
### Theory and methods
[Bayesian statistics: Wikipedia page](https://en.wikipedia.org/wiki/Bayesian_statistics)
Andrew Gelman et al., _Bayesian Data Analysis_ (third edition) [@Gelman_etal_2014].
Robert Grant (2018-08-07) [How (not) to introduce newcomers to Bayesian analysis](https://robertgrantstats.wordpress.com/2018/08/07/how-not-to-introduce-newcomers-to-bayesian-analysis/)
#### R-based
Richard McElreath, 2016, _Statistical Rethinking: A Bayesian Course with Examples in R and Stan_ [@McElreath_2016].
* [{rethinking}](https://github.com/rmcelreath/rethinking), the companion R package
* [McElreath's YouTube channel](https://www.youtube.com/channel/UCNJK6_DZvcMqNSzQdEkzvzA), with Statistical Rethinking lectures
Jim Albert, _Bayesian Computation with R_ [@Albert_2009]
David Robinson, [_Introduction to Empirical Bayes_](https://gumroad.com/l/empirical-bayes)
* [github page](https://github.com/dgrtwo/empirical-bayes-book/blob/master/beta-distribution.Rmd)
* [book announcement](http://varianceexplained.org/r/empirical-bayes-book/) (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)"](http://varianceexplained.org/r/simulation-bayes-baseball/)
Daniel Lüdecke, 2018-06-06, [R functions for Bayesian Model Statistics and Summaries](https://strengejacke.wordpress.com/2018/06/06/r-functions-for-bayesian-model-statistics-and-summaries-rstats-stan-brms/)
Rasmus Bååth (2019-07-15) [Get up to speed with Bayesian data analysis in R](https://docs.google.com/presentation/d/1Lv5_IBi_PXbtp8FbA8-qBI0PwJAvPlP9OZ-6t6l6gwM/edit#slide=id.p), from UseR2019
#### other
Tarek Amr, ["Experimenting the Bayesian way"](https://www.datascience.com/blog/experimenting-the-bayesian-way); summary of Bayesian approach with Python examples (2018-07-18)
### R
Arranged by package
#### `{bayestestR}`
**package**
GitHub page: [bayestestR: Utilities for analyzing Bayesian models and posterior distributions](https://github.com/easystats/bayestestR)
**articles**
easystats (2019-04-15) [Describe and understand Bayesian models and posteriors using bayestestR](https://easystats.github.io/blog/posts/bayestestr_presentation/)
#### `{HydeNet}`
CRAN: [HydeNet: Hybrid Bayesian Networks Using R and JAGS](https://cran.r-project.org/web/packages/HydeNet/)
Vignette: [Decision Network (Influence Diagram) Analyses in HydeNet](https://cran.r-project.org/web/packages/HydeNet/vignettes/DecisionNetworks.html)
#### `{rjags}`
**package**
CRAN page: [rjags: Bayesian Graphical Models using MCMC](https://cran.r-project.org/package=rjags)
**articles**
Alicia Johnson: [Bayesian modeling with {rjags}] {link to DataCamp course removed}
#### `{tidybayes}`
**package**
CRAN page: [tidybayes: Tidy Data and 'Geoms' for Bayesian Models](https://cran.r-project.org/package=tidybayes)
GitHub page: [Bayesian analysis + tidy data + geoms (R package)](https://github.com/mjskay/tidybayes)
**articles**
Matthew Kay, [tidybayes: Bayesian analysis + tidy data + geoms](http://mjskay.github.io/tidybayes/)
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