Course 12 Bayesian Analysis
Bayesian analysis is a robust statistical framework incorporating prior knowledge and observed data to make inferences about unknown parameters. Unlike traditional frequentist methods, which focus solely on the likelihood of data given a parameter, Bayesian methods treat parameters as random variables and update beliefs about them as new data becomes available. This dynamic approach, rooted in Bayes’ Theorem, provides a natural way to quantify uncertainty, allowing for more flexible and interpretable models. Bayesian analysis has become an essential tool in various fields, including machine learning, economics, and epidemiology, due to its ability to handle complex models and incorporate prior expertise into decision-making.