Applied Bayesian MCMC for Parameter Estimation

Unlock powerful techniques to estimate unknown parameters in your models with confidence, even in the face of complex data and uncertainty. This course will equip you with the foundational knowledge and practical skills to implement and interpret Bayesian Markov Chain Monte Carlo (MCMC) methods, enabling you to make more informed decisions based on your data. What you'll learn: * Understand the core principles of Bayesian inference, including Bayes' Theorem, priors, likelihoods, and posteriors. * Learn the fundamentals of Markov Chain Monte Carlo (MCMC) algorithms, focusing on the Metropolis-Hastings algorithm. * Apply MCMC techniques to estimate parameters for various statistical models using practical, step-by-step examples. * Interpret MCMC output, including trace plots, posterior distributions, and convergence diagnostics to assess model fit. * Practice setting up and evaluating simple Bayesian models for real-world parameter estimation challenges. * Explore how to assess model performance and understand credible intervals for parameter uncertainty. The course begins with an introduction to Bayesian statistics and its advantages, then delves into the theory and practical application of MCMC algorithms. You will progress through setting up models, running simulations, and interpreting the results through guided textual explanations and code snippets. This course is designed for absolute beginners with no prior experience in Bayesian statistics or MCMC. A basic understanding of probability and algebra is helpful but not strictly required, as key concepts are explained from the ground up. Start your journey into robust parameter estimation with Bayesian MCMC today.