Bayesian Statistics: Techniques and Models

This is the second course in a two-part series on Bayesian statistics fundamentals. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which uses simple conjugate models to introduce Bayesian methods. To arrive at realistic conclusions, real-world data frequently necessitates the use of more sophisticated models.

This course aims to broaden the "Bayesian toolbox" by introducing more general models and computational methods for fitting them. You'll go over Markov chain Monte Carlo (MCMC) methods in particular, which allow sampling from posterior distributions that don't have an analytical solution. You will work with the open-source, freely available software R and JAGS (some experience with R is assumed, such as having completed the previous course in R) (no experience required). To answer scientific questions involving continuous, binary, and count data, you'll learn how to build, fit, assess, and compare Bayesian statistical models.

To create an active learning experience, this course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards. The lectures cover some basic mathematical concepts, as well as explanations of the statistical modeling process and a few common statistical modeling techniques. Computer demonstrations give you a step-by-step walkthrough. After completing this course, you will have access to a variety of Bayesian analytical tools that can be tailored to your specific data.

This course offers:

• Flexible deadlines: Reset deadlines based on your availability.
• Shareable certificate: Get a Certificate when you complete
• 100% online
• Course 2 of 5 in the Bayesian Statistics Specialization
• Intermediate level
• Approx. 30 hours to complete
• Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Course ratings: 4.8/5

Enroll here: https://www.coursera.org/learn/mcmc-bayesian-statistics