Hey y'all. Welcome to the OSF home for the workshop! You can find the original link from Physalia-courses [here](https://www.physalia-courses.org/courses-workshops/course46/), and the related link to my personal website [here](https://solomonkurz.netlify.app/workshop/intro_bayes_march_2023/). In this OSF project, you'll find the supporting files from the workshop. Here's the original course description: ## Overview We are entering the Golden Age of Bayesian statistics. Thanks to fast personal computers and powerful free software (e.g., [Stan](https://mc-stan.org/)), working scientists can fit an array of Bayesian models tailored to their specific needs. Recent textbooks from authors like McElreath (2015, 2020) have also made Bayesian methods more accessible to applied researchers with minimal backgrounds in mathematics. However, many graduate programs still do not offer introductory courses on Bayesian statistics. To help fill that pedagogical gap, this course is designed to provide an accessible and applied introduction to Bayesian data analysis for a wide variety of linear models using user-friendly [**brms**](https://cran.r-project.org/web/packages/brms/index.html) **R** package. ## Prerequisites We assume familiarity with [**R**](https://cran.r-project.org/), regression, and the Generalized Linear Model (e.g., logistic regression, Poisson regression). Participants will benefit most if they have some experience with multilevel models. No knowledge of calculus or linear algebra is assumed, but basic school level mathematics knowledge is assumed. Most of the **R** code will follow the [**tidyverse** style](https://style.tidyverse.org/). ## Outcomes After completing this course, the participants will: 1. have become familiar with the basics of Bayesian inference, 2. be able to fit a range of regression models with several likelihood functions, 3. be able fit several robust models and distributional models, 4. know how to select priors for their models using prior predictive checks, 5. know how to assess the descriptive accuracy of a model using posterior predictive checks, and 6. know how to express their posterior distributions as effect sizes and informative figures. ## Background reading This course will draw from several introductory textbooks, such as: * [Gelman et al (2020)](https://avehtari.github.io/ROS-Examples/), [Kruschke (2015)](https://sites.google.com/site/doingbayesiandataanalysis/), [McElreath (2015, 2020)](https://xcelab.net/rm/statistical-rethinking/), and [Nicenboim et al (2022)](https://vasishth.github.io/bayescogsci/book/). * I have released several free ebooks which translate other textbooks into **brms** and **tidyverse**-style code, which you can find [here](https://solomonkurz.netlify.app/book/). * For a conceptual introduction to Bayesian data analysis, check out the 2-hour lecture by McElreath, "[Bayesian Inference is Just Counting](https://youtu.be/_NEMHM1wDfI)". * For basic **R** programming with **tidyverse** methods, we recommend the free ebook by [Grolemund & Wickham (2017)](https://r4ds.had.co.nz/). * For introductions to the **brms** package, we recommend the reference manual and the several vignettes listed on the [**brms** CRAN page](https://cran.r-project.org/web/packages/brms/index.html).