Bayesian MCMC for Parameter Estimation

Estimating unknown parameters from observed data is a fundamental challenge across science and engineering. Traditional statistical methods often struggle with complex models or limited data, leaving uncertainty about your conclusions. This course will equip you with a robust framework for parameter estimation: Bayesian Markov Chain Monte Carlo (MCMC). You will gain the skills to build, run, and interpret MCMC simulations, enabling you to quantify uncertainty and make data-driven decisions with confidence. What you'll learn: * Understand the foundational principles of Bayesian inference and its advantages for parameter estimation. * Learn how Markov Chain Monte Carlo (MCMC) algorithms work, including the Metropolis-Hastings sampler. * Apply MCMC techniques to estimate parameters in various statistical models using computational methods. * Interpret MCMC output through essential diagnostic checks to ensure sampler convergence and reliability. * Explore conceptual approaches to leveraging modern MCMC samplers and probabilistic programming patterns for efficient model fitting. * Quantify uncertainty in parameter estimates and present Bayesian results effectively. The course begins with a clear introduction to Bayesian probability and statistical modeling, progressing through the theoretical underpinnings of MCMC before diving into practical application and interpretation. This course is designed for beginners with a basic understanding of probability and statistics, who are eager to learn modern computational methods for parameter estimation. Start your journey into Bayesian MCMC and enhance your data analysis capabilities.