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Wiley-VCH - Mathematics
A Passion for Publishing
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Decision Analytics
Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise across healthcare, finance, 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. Readers will also find: * Coverage of artificial intelligence techniques applied to sequential decision-making problems * Monte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulations * Python code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presented * Practice exercises with solutions and an instructor's manual designed to support both self-study and classroom-based teaching * A concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed 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 a unified treatment of sequential decision-making methods. [240 Pages, Hardcover]
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Algebra I Workbook For Dummies with Online Practice
[320 Pages, Softcover]
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Causal Artificial Intelligence
[384 Pages, Hardcover]
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Differential Equations in the Era of Advanced Computational Techniques
[592 Pages, Hardcover]
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Mathematical Foundations for Chemistry, Physics, and Engineering
Bridge abstract mathematics with chemistry, physics, and engineering problem-solving Students in chemistry, physics, and materials science often struggle to connect abstract mathematics with real physical applications. Mathematical Foundations for Chemistry, Physics, and Engineering presents complex mathematical ideas in a student-friendly, step-by-step style, ensuring readers never lose sight of the molecule. Across fourteen carefully structured chapters, the book progresses logically from vectors, linear vector spaces, and matrix characteristics, through group theory, Fourier and Laplace transforms, to special functions and partial differential equations. Designed specifically to bridge the gap between pure mathematics and the physical sciences, this comprehensive text introduces mathematical problems first, followed immediately by practical applications in quantum mechanics, spectroscopy, and chemical kinetics It includes unique pedagogical features like Fact Boxes and Red Flags to guide learners. The book offers: * A logical progression across fourteen chapters, moving from vector spaces and matrices to partial differential equations * Practical applications drawn directly from physical chemistry, quantum mechanics, molecular spectroscopy, and advanced chemical kinetics * Comprehensive coverage of group theory, Fourier transforms, and Laplace transforms tailored for scientific problem-solving. * Student-friendly features including Math Textboxes, Fact Boxes, and Red Flags to prevent common mistakes * Twelve extensive appendices containing standard character tables, transform properties, special function formulas, and vital numerical methods Designed for upper undergraduate and postgraduate students in chemistry, physics, and materials science, this textbook also serves engineering and molecular biology majors. It equips readers with the essential analytical tools needed to confidently tackle complex problems across the physical sciences. [560 Pages, Hardcover]
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