Bayesian MCMC for Parameter Estimation in Engineering

Understanding model parameters is crucial for informed decision-making, yet traditional methods often struggle to fully quantify uncertainty. This course will equip you with the foundational knowledge and practical skills to apply Bayesian inference and Markov Chain Monte Carlo (MCMC) methods, enabling you to estimate parameters, quantify uncertainty, and build more robust models. What you'll learn: * Understand the core principles of Bayesian statistics, including likelihood, prior, and posterior distributions. * Learn to formulate Bayesian models for various parameter estimation problems. * Master the foundational concepts of Markov Chain Monte Carlo (MCMC) algorithms, including Metropolis-Hastings. * Apply MCMC techniques to sample from complex posterior distributions. * Interpret MCMC results, diagnose convergence, and assess model fit using textual diagnostics. * Quantify parameter uncertainty and make robust predictions based on Bayesian models. * Explore conceptual approaches to efficient MCMC sampling for challenging problems. The course begins with a thorough introduction to Bayesian probability theory and builds progressively towards practical MCMC implementation, concluding with guidance on interpreting results and advanced considerations. This course is designed for beginners in engineering, data science, and scientific computing who want to learn Bayesian inference and MCMC from scratch, with no prior knowledge of these topics required. Start your journey into powerful Bayesian modeling today.