Wananga landing Wananga landing

Bayesian Statistics Using R

15 January 2024

'Bayesian Statistics Using R' is a free, online course that introduces the fundamentals of Bayesian approach to data analysis, followed by a deep dive into its richness and flexibility.


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.
Privacy Preferences

By clicking "Accept All Cookies", you agree to the storing of cookies on your device to enhance site navigation, analyse site usage, and assist in our marketing efforts.