DASC 536 - Bayesian Statistics

3 Credit Hours
3 Lecture Hours
0 Laboratory Hours

An introduction to the basic ideas of Bayesian statistics. In Bayesian statistics, population parameters are considered random variables having probability distributions. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities using the observed data. You will be introduced to the basic Bayesian concepts and computational techniques. We will also compare and contrast the Bayesian methods with comparable classical (frequentist) techniques. The course emphasizes data analysis through practical applications using statistical software.

Prerequisites: DASC 531 or STAT 375.