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Bayesian Statistics Using R
Professional Certificate programs are series of courses designed by industry leaders and top universities to build and enhance critical professional skills needed to succeed in todays most in-demand fields.
Free
This Course Includes
edx
0 (0 reviews )
3 months at 5 - 10 hours per week
english
Online - Self Paced
professional certificate
University of Canterbury
About Bayesian Statistics Using R
UCx's Bayesian Statistics Using R Professional Certificate
Introduction to Bayesian Statistics Using R
Advanced Bayesian Statistics Using R
Job Outlook
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..