Decision Analytics by Brian T. Denton (.ePUB)+
File Size: 17 MB
Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making by Brian T. Denton
Requirements: .PDF/.ePUB reader, 17 mb
Overview: Mathematical models and algorithms for sequential decisions under uncertainty
Sequential 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. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, 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.
Across ten chapters, Decision Analytics 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.
Genre: Non-Fiction > Tech & Devices
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