Bayesian Statistics: Techniques and Models

( 65 Reviews )

Created by: Matthew Heiner

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 94 / 100

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

The Data Scientist's Toolbox (2019)

5.0 (792 Reviews)

Provider: Coursera
Time: 8h

Free

Python for Data Science (2017)

4.4 (9 Reviews)

Provider: edX

Free

Python A-Z: Python For Data Science With Real Exercises!

Python A-Z: Python For Data Science With Real Exercises! (2021)

4.5 (472 Reviews)

Provider: Udemy
Time: 11h

11.99

More Courses

Reviews

4.7

65 total reviews

5 star 4 star 3 star 2 star 1 star

Write a Review

By Sanjeev G on 13-Jun-17

This course is a perfect continuation of the Bayesian Statistics course by Prof. Herbert Lee. It's not only mathematically rigorous but also very applied. Excellent for the beginners to the Bayesian Statistics as it allows to start confidently using Bayesian models in practice. Matthew Heiner is an excellent lecturer. Thank you.

By Rajib R on 29-Dec-17

Great course!

By PrasadTheertha on 11-Nov-17

The course requires good understanding of Bayesian methods and linear modelling, something that is covered in previous course of this track from University of California Santa Cruz.All quizes are quite easy to complete after watching the videos, but don't be fooled by this apparent simplicity - there is much more to the class than just that.Capstone project is challenging and does put to test all of the topic discussed in class,discussion forums are very helpful and also are extremely interesting to read.I can strongly recommend this class to anyone who is interested in Bayesian Methods.I've seen quite a few of similar classes on Coursera, but this one is the best, in my opinion, but also is the hardest one.Do not miss out on Honors track, recommended supplementary reading and Capstone - those are the gems.

By Venu G K on 31-Jul-18

Great course to learn both theories and techniques!

By Toan N on 30-Jul-18

A very good practical and theoretical course

By Jiasun on 20-Jul-19

Not enough depth.

By Sathish R on 21-May-18

This course is taught in a way that not useful for real world applications.

By Sandra M on 14-May-18

Good course, but the peer review process for the Capstone project in Week 5 is broken. Based on submissions to the course Forum in which multiple students have submitted their work on time but not received a grade due to lack of peer reviewers, this has been going on .

By Chiu W K on 29-Jul-17

Informative but the pace is slow

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 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 zhen w on 28-Jul-17