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)
Course Overview
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Skills You'll Learn
Data Analysis, Probability, Regression Analysis, Simulations, Markov Model, Probability Distribution, Statistical Modeling, Bayesian Statistics, Statistical Inference, Statistical Software, Statistical Analysis, Statistical Programming, Statistical Methods, R Programming
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 (Sarah) Jamshidi