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Bayesian Statistics: Techniques and Models

4.7

Course Description

This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our "Bayesian toolbox" with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data.

Time: 36h

Instructor Details

Matthew Heiner

4.7 Rating
65 Reviews

CoursesBayesian Statistics: Techniques and Models

Reviews

4.7

65 reviews in total

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By Arkobrato G on 11-Nov-19

Great course with challenging assignments and de

By Hyun J K on 13-Oct-19

Perfect combination of theory part + application partRecommend to people who took the basic Bayesian class

By 兰茜 on 11-Oct-19

Thank you!

By Jayanand S on 17-Sep-19

Complex subject made easy with easy to understand theory & practical examples

By Madayan A on 4-Sep-19

Very good course, a little bit to slow at some point but this is marginal in the overall feeling.

By Gustavo M on 26-Aug-19

Very nice course. A bit more theory on sampling methods would be welcome.

By Jiasun on 20-Jul-19

Not enough depth.

By dhirendra k on 15-Jul-19

Very good part II course in continuation with course I. The trainer provided good and detailed explanations throughout the course. Also lot of scenarios covered with help of practical examples. Very much recommended course in Bayesian Theory

By Eugene B on 26-Jun-19

The course provided a lot of very helpful tools. However, I believe it was a bit too fast paced. Furthermore, there were certain topics which were not explained clearly -- for example, the discussion of the Metropolis-Hastings Algorithm and Gibbs Sampling was extremely confusing.

By Yahia E G on 6-Jun-19

Really good intermediate introduction to bayesian analysis. I really liked how hands-on the course is. The last project was very useful as one will likely to face challenges and try to solve them especially if you use a rich dataset.

By Luis A A C on 6-Jun-19