Bayesian Statistics for Chemical Engineering

Chemical engineering problems often involve inherent uncertainties in data and model parameters. Learning to quantify and manage this uncertainty is crucial for making informed decisions and developing robust solutions. This course equips you with the foundational knowledge of Bayesian statistics to confidently approach real-world chemical engineering challenges, enabling robust parameter estimation and insightful model analysis. What you'll learn: * Understand the foundational concepts of Bayesian probability and inference. * Formulate Bayesian models for effective parameter estimation in engineering. * Apply common prior and likelihood distributions to construct robust models. * Interpret posterior distributions to quantify uncertainty and draw conclusions. * Grasp the fundamental principles of Markov Chain Monte Carlo (MCMC) sampling. * Practice applying Bayesian methods to solve typical chemical engineering problems. The course begins with an introduction to Bayesian probability and its fundamental concepts, progressing to practical application in parameter estimation, model comparison, and uncertainty analysis. This course is designed for beginner chemical engineers, students, and professionals with no prior experience in Bayesian statistics. Start your journey to mastering Bayesian methods for engineering analysis today.