Master Bayesian inference and unlock powerful probabilistic reasoning for data-driven decision-making. This course builds your foundation in Bayesian analysis, from viewing probability as degrees of belief to implementing advanced MCMC methods. Learn to apply Bayes’ theorem to real-world problems, use conjugate priors for efficient computation, and derive credible intervals that fully capture parameter uncertainty. Through hands-on practice, you’ll move from analytical solutions to computational techniques like Metropolis-Hastings, Gibbs sampling and Variational Inference, essential for modern Bayesian workflows. You’ll gain skill in interpreting posterior distributions, contrasting Bayesian and frequentist perspectives, and applying convergence diagnostics for reliable results. Whether in finance, healthcare, or business, you’ll acquire the statistical framework and computational tools to make principled inferences under uncertainty and effectively communicate probabilistic insights.