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- Title Dynamic Programming and Bayesian Inference, Concepts and Applications
- Author(s) Thomas J. Sargent and John Stachurski
- Publisher: Self-Publishing (GitHub), 2024;
- Hardcover: N/A
- eBook: PDF
- Language: English
- ISBN-10: N/A
- ISBN-13: N/A
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This book is about Dynamic Programming and its applications in economics, finance, and adjacent fields. It brings together recent innovations in the theory of dynamic programming and provides applications and code.
About the Authors- N/A
- Operations Research (OR), Linear Programming, Optimization, Approximation, etc.
- Statistics and Mathematical Statistics
- Applied Mathematics
- Computational and Algorithmic Mathematics
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Dynamic Programming and Bayesian Inference, Concepts/Apps
This is a book on the far-ranging algorithmic methododogy of Dynamic Programming. It presents a comprehensive and rigorous treatment of dynamic programming, and provides some applications of Bayesian optimization and dynamic programming.
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Genetic Algorithm Afternoon: A Guide for Software Developers
Are you a software developer looking to harness the power of Genetic Algorithms (GAs) to solve complex optimization problems? This book is your go-to resource for mastering this innovative and powerful technique.
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Particle Swarm Afternoon: A Guide for Software Developers
Are you a software developer looking to harness the power of Particle Swarm Optimization (PSO) to solve complex optimization problems? This book is your go-to resource for mastering this innovative and powerful technique.
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Simulated Annealing Afternoon: A Guide for Software Developers
Are you a software developer looking to harness the power of Simulated Annealing (SA) to solve complex optimization problems? This book is your go-to resource for mastering this innovative and powerful technique.
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Convex Optimization for Machine Learning (Changho Suh)
This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal is to help develop a sense of what convex optimization is, and how it can be used.
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Design of Heuristic Algorithms for Hard Optimization
This open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the techniques discussed.
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Algorithms for Optimization (Mykel J. Kochenderfer, et al.)
A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. Readers will learn about computational approaches for a range of challenges.
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Sine Cosine Algorithm for Optimization (Jagdish Bansal, et al.)
This book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of Metaheuristics algorithms.
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Introduction to Online Convex Optimization (Elad Hazan)
This book presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed.
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Convex Optimization (Stephen Boyd, et al.)
On recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
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Optimization for Decision Making: Linear and Quadratic Models
This book illustrates how to formulate real world problems using linear and quadratic models; It focus on developing modeling skills to support valid decision making for complex real world problems.
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Global Optimization Algorithms - Theory and Application
This book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on Evolutionary Computation by discussing evolutionary algorithms, genetic algorithms, Genetic Programming, etc.
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Inventory Analytics: A Practicable, Python-Driven Approach
This book provides a comprehensive and accessible introduction to the theory and practice of Inventory Control - a significant research area central to supply chain planning. It adopts a practicable, Python-driven approach to illustrating theories and concepts.
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Algorithms for Decision Making (Mykel Kochenderfer, et al)
This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.
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