edX
edXBayesian Statistics Using R
356 studentsBEGINNEREnglish
Korshub's take
Statistics Through a Bayesian Lens
Frequentist statistics dominates textbooks, but Bayesian reasoning matches how humans actually think: update beliefs as evidence arrives. This beginner course in R teaches you that framework from Bayes' Theorem through production-ready inference.
You'll work with conjugate models, Bayesian regression and ANOVA, then progress to MCMC methods and model diagnostics. The course emphasizes answering your actual research question rather than mechanically applying methods. Latent variables, missing data, multivariate analysis, and model averaging extend your toolkit for messy reality.
About this course
Bayes’ Theorem. Differences between classical (frequentist) and Bayesian inference. ,Posterior inference: summarizing posterior distributions, credible intervals, posterior probabilities, posterior predictive distributions and data visualization. ,Gamma-poisson, beta-binomial and normal conjugate models for data analysis. ,Bayesian regression analysis and analysis of variance (ANOVA). ,Use of simulations for posterior inference. Simple applications of Markov chain-Monte Carlo (MCMC) methods and their implementation in R. ,Bayesian cluster analysis. ,Model diagnostics and comparison. ,Make sure to answer the actual research question rather than “apply methods to the data” ,Using latent (unobserved) variables and dealing with missing data. ,Multivariate analysis within the context of mixed effects linear regression models. Structure, assumptions, diagnostics and interpretation. Posterior inference and model selection. ,Why Monte Carlo integration works and how to implement your own MCMC Metropolis-Hastings algorithm in R. ,Bayesian model averaging in the context of change-point problem. Pinpointing the time of change and obtaining uncertainty estimates for it.
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