Bayesian Parameter Estimation for Chemical Engineers

In chemical engineering, predicting reaction behavior requires accurate model parameters, yet experimental data is always subject to uncertainty. This course introduces you to Bayesian parameter estimation, helping you quantify uncertainty and build reliable dynamic reaction models. You will transition from basic statistical concepts to implementing robust estimation algorithms in MATLAB through clear, written explanations and structured code walk-throughs. What you'll learn: - Understand the core principles of Bayesian statistics and parameter estimation in chemical systems - Formulate dynamic reaction models and set up systems of differential equations - Implement numerical methods in MATLAB to solve dynamic systems and calculate predicted values - Apply basic sampling methods to estimate parameter probability distributions - Analyze uncertainty and perform basic sensitivity analysis on your reaction models - Interpret estimation results to make data-driven decisions in chemical process design The course begins with foundational concepts of probability, chemical kinetics, and MATLAB basics, then guides you step-by-step through setting up dynamic models, writing estimation scripts, and analyzing experimental data. Designed for chemical engineering students, researchers, and practicing engineers new to Bayesian methods, this course requires no advanced statistical background, though basic familiarity with MATLAB and reaction kinetics is helpful. Start mastering Bayesian parameter estimation and bring rigorous uncertainty analysis to your chemical engineering projects today.