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 3 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 3: Multiparameter Models
This module provides an overview of Bayesian inference for multiparameter models, focusing on handling normal data, employing conjugate priors, and applying multivariate normal models to practical scenarios.
Taught By
Shahrzad Jamshidi