Bayesian Statistics Using R

Free online course introducing the fundamentals of Bayesian approach to data analysis, followed by a deep dive into its richness and flexibility. Join UCx on edX to enrol! 

Title: Bayesian Statistics Using R 
Instructor: Professor Elena Moltchanova
Start Date: Enrol now
Price: Free 

What you will learn

  • 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.