Bayesian Statistics Certification Course Part 2 : Techniques and Models

Bayesian Statistics Certification Course Part 2 : Techniques and Models ranks first in the list of best online probability & statistics courses. This is the second course in a two-part series that introduces the fundamentals of Bayesian statistics. It is a continuation of the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through the use of simple conjugate models. Real-world data frequently necessitates more sophisticated models in order to reach realistic conclusions. This course seeks to broaden "Bayesian toolbox" by introducing more general models and computational techniques for fitting them. They focus on Markov chain Monte Carlo (MCMC) methods, which enable sampling from posterior distributions with no analytical solution. They use the open-source, freely available software R (some experience is assumed, such as having completed the previous R course) and JAGS (no experience required).

You'll learn how to build, fit, evaluate, and compare Bayesian statistical models to answer scientific questions using continuous, binary, and count data. To create an active learning experience, this course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards. The lectures cover some of the fundamental mathematical concepts, as well as explanations of the statistical modeling process and a few basic modeling techniques used by statisticians. Computer demonstrations provide concrete, hands-on instructions. After completing this course, you will have access to a wide range of Bayesian analytical tools that are customizable to your data.

Skills you will gain:

• Gibbs Sampling, Bayesian Statistics, Bayesian Inference, R Programming

It is sub divided in the following format:

• Statistical modeling and Monte Carlo estimation
• Markov chain Monte Carlo (MCMC)
• Common statistical models
• Count data and hierarchical modeling
• Capstone Project

Rating: 4.8/5.0

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