BAYESIAN METHODS IN DATA ANALYSIS AND DECISION MAKING

Catalog description

An introduction to Bayesian methods for analyzing data and making decisions under uncertainty. Topics include Bayesian probability concepts, prior and posterior distributions, credible intervals, and Bayesian hypothesis testing. Key areas of focus are Bayesian modeling, hierarchical models, and simulation-based methods such as Markov Chain Monte Carlo (MCMC). The course also covers decision theory elements, including loss functions, risk assessment, and utility optimization. Techniques are applied to real-world scenarios in finance and economics to illustrate practical decision-making strategies.