Bayesian Probability and Decision Making for Engineering

Engineering decisions often require making critical choices under deep uncertainty, where historical data is scarce but the consequences of failure are high. This text-only course introduces you to the core principles of probability and statistics, focusing on how to update your beliefs and design robust experiments using Bayesian analysis. You will learn how to systematically evaluate risks, interpret new data, and make optimal engineering decisions. What you'll learn: - Understand the foundational rules of probability, conditional probability, and the core mathematics of Bayes' theorem. - Apply Bayesian updating to incorporate new experimental evidence into existing engineering risk models. - Evaluate the value of information when designing engineering tests and data-gathering campaigns. - Model engineering uncertainties using modern statistical concepts and decision trees. - Analyze hypothetical scenarios and extreme-event probabilities to assess structural or environmental safety. You will begin by mastering basic probability definitions, terminology, and the mathematical framework of prior and posterior probabilities. From there, you will read through practical engineering scenarios, exploring how to structure decision-making processes when faced with incomplete information. This course is designed for engineering students, practicing civil and environmental engineers, and technical professionals who want a clear, conceptual understanding of Bayesian statistics without complex prerequisites. Start reading today to master the statistical tools needed for modern engineering risk analysis.