13. Bayes Rules, Bayesian Inference and Applications

Topics, Class Notes and Code		Assignments and Side Readings
Bayes Rule for Dependent Events Compoents: prior probability, likelihood, posterior probability Bayes rule as a predictive model Bayes Rule for Categorical Variables Likelihood-based parametric Bayes rule as predictive model Bayes Rule for Continuous Variables Parameter estimation of normal parameters using Gaussian prior distribution Baysian inference based on posterior distribution Concept of naive Bayes prediction Notes and code Lecture Note: [HTML]		Optional Reading Assignments Bayesian Statistics - Principles and Benefit [PDF] Understanding the Differences Between Bayesian and Frequentist Statistics [PDF] Comparison of Frequentist and Bayesian Inference [PDF] A Book Chapter from CMU (the first three section) [PDF] Written Assignment No assignment for this week. Please start review the contents covered in this semester. Leature notes and your weekly assignments are highly relevant to the final exam.