Bayesian Parameter Estimation for Chemical Engineering

Accurate parameter estimation is critical for designing, scaling, and optimizing chemical processes, yet experimental data is always accompanied by uncertainty. Traditional fitting methods often fail to capture this uncertainty, which is where Bayesian statistics and numerical parameter estimation become essential.\n\nThis text-only course guides you through the foundational concepts of Bayesian inference applied to chemical engineering systems. You will learn how to formulate prior distributions, construct likelihood functions, and numerically compute posterior distributions to make reliable engineering decisions.\n\nWhat you'll learn:\n- Understand the core principles of Bayesian statistics and probability in chemical engineering contexts\n- Formulate prior distributions and likelihood functions based on experimental process data\n- Compute posterior distributions numerically to estimate model parameters\n- Quantify uncertainty in chemical kinetics, thermodynamics, and transport models\n- Apply modern computational workflows, including basic Markov Chain Monte Carlo (MCMC) concepts, to handle multi-response systems\n- Analyze parameter sensitivity and model fit using numerical diagnostics\n\nBeginning with foundational probability theory and Bayesian definitions, the course progresses systematically through likelihood formulation, numerical integration techniques, and practical parameter estimation workflows using MATLAB-compatible algorithms.\n\nThis course is designed for undergraduate chemical engineering students, practicing engineers, and researchers new to Bayesian methods. No advanced statistical background is required, though a basic familiarity with calculus and introductory programming is helpful.\n\nStart mastering Bayesian parameter estimation to make more robust, data-driven engineering decisions today.