STAT 220 - Bayesian Data Analysis Learning Course

[STAT 220 - Bayesian Data Analysis Learning Course] STAT 220 - Bayesian Data Analysis STAT 220 introduces students to Bayesian statistical methods, which provide a framework for updating beliefs about unknown quantities based on observed data and prior knowledge. The course covers topics such as Bayesian inference, Bayesian hierarchical models, Markov chain Monte Carlo (MCMC) methods, and Bayesian model comparison. Students learn how to specify prior distributions, estimate posterior distributions, and make probabilistic predictions using Bayesian techniques. Through theoretical foundations and practical applications, students gain skills in Bayesian reasoning, model uncertainty, and decision-making under uncertainty. STAT 220 prepares students for advanced coursework and research in Bayesian statistics, machine learning, and applications in fields such as healthcare, finance, and environmental sciences.This Learning Course is a platform that helps students to study and learn. The platform provides all the required material for the students to study. The material includes Study guide, Exam Questions & Answers, Study Notes and other resources that are useful for the students. It also gives information about the upcoming exams so that students can prepare themselves accordingly.