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)
Module 4 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 4: Large-Sample Inference and Frequency Properties
This module provides an understanding of large-sample inference and frequency properties in Bayesian analysis, focusing on normal approximations, large-sample theory, and the evaluation of Bayesian methods from a frequentist perspective.
Taught By
Shahrzad Jamshidi