Bayesian Thinking: Case Studies and AI Tools to Support Data-Driven Decisions in Engineering, Science, and Business

Apply Bayesian analysis to real engineering, science, and business problems Professionals facing uncertain data need a complete workflow from problem framing to defensible decisions.Bayesian Thinking: Case Studies and AI Tools to Support Data-Driven Decisions in Engineering, Science, and Business delivers that workflow through case studies in reliability engineering, medical diagnostics, economics, and product quality.Readers master every stage: framing real-world questions as probabilistic models, eliciting domain-informed priors, selecting models via Bayes factors and WAIC, running inference in R and Stan, and applying statistical decision theory to optimize actions.The book shows how to partner with AI for prompt engineering, model exploration, and sensitivity analysis while preserving human judgment and rigor.Clear code snippets, visual diagnostics, and posterior predictive checks build intuition fast.You will learn to optimize maintenance schedules, evaluate diagnostic tests, mode