{"product_id":"9781394345786","title":"Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making","description":"\u003ch1\u003eDecision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making\u003c\/h1\u003e \u003ch2\u003eDenton, Brian T.\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMathematical models and algorithms for sequential decisions under uncertainty\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eSequential decisions under uncertainty arise in  many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. \u003ci\u003eDecision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making\u003c\/i\u003e, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. \u003c\/p\u003e\u003cp\u003eAcross ten chapters, \u003ci\u003eDecision Analytics\u003c\/i\u003e covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructor's solution manual support classroom adoption. \u003c\/p\u003e\u003cp\u003eReaders will also find: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eCoverage of artificial intelligence techniques applied to sequential decision-making problems\u003c\/li\u003e \u003cli\u003eMonte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulations\u003c\/li\u003e \u003cli\u003ePython code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presented\u003c\/li\u003e \u003cli\u003ePractice exercises with solutions and an instructor's manual designed to support both self-study and classroom-based teaching\u003c\/li\u003e \u003cli\u003eA concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eDesigned for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Wiley-IEEE Press\u003c\/p\u003e \u003cp\u003ePublication Date: 2026-11-24\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9781394345786\u003c\/p\u003e \u003cp\u003eDOI: \u003c\/p\u003e \u003cp\u003eDimensions: cm xcm\u003c\/p\u003e \u003cp\u003ePages: 256\u003c\/p\u003e ","brand":"Wiley","offers":[{"title":"Default Title","offer_id":46536393949324,"sku":"9781394345786","price":126.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781394345786.jpg?v=1779496385","url":"https:\/\/lateknightbooks.com\/products\/9781394345786","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}