Skip to main content

Advanced Bayesian Statistics Using R

Now that you know the basics of Bayesian inference, dive deeper to explore its richness and flexibility more fully. Let’s take a closer look at modeling latent variables, Bayesian model averaging, generalised linear models, and MCMC methods

...

There is one session available:

414 already enrolled!
After a course session ends, it will be archivedOpens in a new tab.
Starts May 25
Ends Jun 1

Advanced Bayesian Statistics Using R

Now that you know the basics of Bayesian inference, dive deeper to explore its richness and flexibility more fully. Let’s take a closer look at modeling latent variables, Bayesian model averaging, generalised linear models, and MCMC methods

Estimated 6 weeks
5–10 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

After a course session ends, it will be archivedOpens in a new tab.
Starts May 25
Ends Jun 1

About this course

Skip About this course

Advanced Bayesian Data Analysis Using R is part two of the Bayesian Data Analysis in R professional certificate.

This course is directed at people who are already familiar with the fundamentals of Bayesian inference. It explores further the concepts, methods, and algorithms introduced in the part one (Introductory Bayesian Data Analysis Using R).

The course places mixed effects regression models useful for experiments with repeated measures or additional hierarchy often encountered in biostatistics, ecology and health sciences among others within the Bayesian context. It takes a closer look at the Markov Chain Monte Carlo (MCMC) algorithms, why they work and how to implement them in the R programming language. Convergence assessment and visualisation of the results are discussed in some detail. The course also explores Bayesian model averaging, often used in machine learning, all within the context of practical examples.

Finally, we discuss different kinds of missing data, and the Bayesian methods of dealing with such situations.

Prior facility in basic algebra and calculus as well as programming in R is highly recommended.

At a glance

  • Institution: UCx
  • Subject: Data Analysis & Statistics
  • Level: Advanced
  • Prerequisites:

    Prior facility in basic algebra and calculus as well as programming in R is highly recommended.

What you'll learn

Skip What you'll learn

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

About the instructors

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX for Business.