A rigorous introduction to the theory of Bayesian Statistical Inference and Data Analysis, including prior and posterior distributions, Bayesian estimation and testing, Bayesian computation theories and methods, and implementation of Bayesian computation methods using popular statistical software. Required Textbook: Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2013) Bayesian Data Analysis, Third Edition, Chapman & Hall/CRC. Software Requirements: R or Python, Word processing (such as Word, Pages, LaTeX, etc)
Instructor Introduction
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Skills You'll Learn
Bayesian, Bayesian Modeling, Markov Chain Monte Carlo (MCMC), Markov Chain Monte Carlo, Bayesian Statistics, Bayesian Probability, Bayesian Inference
From the lesson
Module 1: Fundamentals of Bayesian Inference
Welcome to MATH 574 Bayesian Computational Statistics! This module covers the ideas of Bayesian inference. It focuses on a framework for Bayesian inference and discusses the general approach to computation.
Taught By
Shahrzad Jamshidi