STAT 120 - Introduction to Bayesian Inference and Applications Learning Course

[STAT 120 - Introduction to Bayesian Inference and Applications Learning Course] STAT 120 - Introduction to Bayesian Inference and Applications STAT 120 provides students with an introduction to Bayesian inference, a statistical approach for quantifying uncertainty using probability distributions. The course covers foundational concepts such as Bayes' theorem, prior and posterior distributions, Markov chain Monte Carlo (MCMC) methods, and Bayesian model comparison. Students learn how to apply Bayesian methods to analyze data, make probabilistic predictions, and update beliefs based on new evidence. Through theoretical principles and practical examples, students develop skills in Bayesian modeling, parameter estimation, and model validation. STAT 120 prepares students for advanced coursework in Bayesian statistics, machine learning, and applications in diverse fields such as finance, healthcare, and engineering.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.