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Professional Certificate in
Bayesian Statistics Using R

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.
Expert instruction
2 skill-building courses
Progress at your own speed
3 months
5 - 10 hours per week
For the full program experience

Courses in this program

  1. UCx's Bayesian Statistics Using R Professional Certificate

Meet your instructor
from University of Canterbury (UCx)

Elena Moltchanova
Professor of Statistics
University of Canterbury

Experts from UCx committed to teaching online learning

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2 courses in 3 months
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