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Course Description

The Bayesian approach to statistics is an alternative to the traditional methods based on hypothesis and significance testing. Bayesian statistics uses prior information and combines it with observable data to quantify knowledge after observing data in what is called the posterior. Simulation is used to approximate this posterior distribution.

Learner Outcomes

By the end of this course, students will be able to...

  • Recognize the differences between Bayesian and classical methods
  • Manage, summarize, and analyze data with R 
  • Apply analytical techniques for Bayesian analysis whenever applicable
  • Apply JAGs, WinBUGS, and Stan to perform Bayesian statistics
  • Apply Bayesian methods to regression including hierarchal models
  • Communicate the results of a study within with Bayesian context

Prerequisites

Understanding of simple and multiple linear regression, Pearson's and non-parametric correlation, analysis of variance (ANOVA), and logistic regression.

Duration

30 Hours
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