Bayesian Statistics for Chemical Engineers

Chemical engineering often involves complex systems with inherent uncertainties. Bayesian statistics provides a powerful framework for incorporating prior knowledge and quantifying these uncertainties, leading to more robust models and informed decisions. This course will equip you with the fundamental concepts of Bayesian inference, enabling you to build robust statistical models and make informed decisions in your engineering work. What you'll learn: * Understand the core principles of probability, conditional probability, and Bayes' theorem * Learn to define and utilize prior and likelihood distributions in Bayesian modeling * Apply Bayesian methods for parameter estimation and hypothesis testing in engineering contexts * Interpret posterior distributions, credible intervals, and model comparison metrics * Practice setting up and analyzing simple Bayesian models for chemical process data * Comprehend the conceptual role of computational techniques like Markov Chain Monte Carlo (MCMC) in modern Bayesian analysis The course begins with a thorough introduction to the probabilistic foundations of Bayesian statistics before progressing to practical applications in parameter estimation and model evaluation. This course is designed for beginner chemical engineers, students, and professionals interested in applying statistical methods to their field, with no prior knowledge of Bayesian statistics required. Start your journey into Bayesian data analysis for chemical engineering today.