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 8 Introduction
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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 8: Advanced Topics
This module covers advanced topics in Bayesian inference, focusing on the setup, interpretation, and application of mixture models, as well as addressing computational challenges and integrating mixture models with multivariate data analysis.
Taught By
Shahrzad (Sarah) Jamshidi