Author : Mohammad Saber Fallah Nezhad
Publisher : BoD – Books on Demand
Page : 168 pages
File Size : 44,69 MB
Release : 2014-04-29
Category : Mathematics
ISBN : 953511364X
Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. Because of these developments, interest in dynamic programming and Bayesian inference and their applications has greatly increased at all mathematical levels. The purpose of this book is to provide some applications of Bayesian optimization and dynamic programming.
Author :
Publisher :
Page : pages
File Size : 12,90 MB
Release : 2014
Category :
ISBN : 9789535150480
Author : Mohammad Saber Fallah Nezhad
Publisher : BoD – Books on Demand
Page : 142 pages
File Size : 30,33 MB
Release : 2018-05-02
Category : Mathematics
ISBN : 1789230926
This book is an introduction to the mathematical analysis of Bayesian decision-making when the state of the problem is unknown but further data about it can be obtained. The objective of such analysis is to determine the optimal decision or solution that is logically consistent with the preferences of the decision-maker, that can be analyzed using numerical utilities or criteria with the probabilities assigned to the possible state of the problem, such that these probabilities are updated by gathering new information.
Author : Warren B. Powell
Publisher : John Wiley & Sons
Page : 573 pages
File Size : 47,91 MB
Release : 2011-10-26
Category : Mathematics
ISBN : 111802916X
Praise for the First Edition "Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners." —Computing Reviews This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems Understanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems involve making decisions in the presence of uncertainty. Approximate Dynamic Programming, Second Edition uniquely integrates four distinct disciplines—Markov decision processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully approach, model, and solve a wide range of real-life problems using ADP. The book continues to bridge the gap between computer science, simulation, and operations research and now adopts the notation and vocabulary of reinforcement learning as well as stochastic search and simulation optimization. The author outlines the essential algorithms that serve as a starting point in the design of practical solutions for real problems. The three curses of dimensionality that impact complex problems are introduced and detailed coverage of implementation challenges is provided. The Second Edition also features: A new chapter describing four fundamental classes of policies for working with diverse stochastic optimization problems: myopic policies, look-ahead policies, policy function approximations, and policies based on value function approximations A new chapter on policy search that brings together stochastic search and simulation optimization concepts and introduces a new class of optimal learning strategies Updated coverage of the exploration exploitation problem in ADP, now including a recently developed method for doing active learning in the presence of a physical state, using the concept of the knowledge gradient A new sequence of chapters describing statistical methods for approximating value functions, estimating the value of a fixed policy, and value function approximation while searching for optimal policies The presented coverage of ADP emphasizes models and algorithms, focusing on related applications and computation while also discussing the theoretical side of the topic that explores proofs of convergence and rate of convergence. A related website features an ongoing discussion of the evolving fields of approximation dynamic programming and reinforcement learning, along with additional readings, software, and datasets. Requiring only a basic understanding of statistics and probability, Approximate Dynamic Programming, Second Edition is an excellent book for industrial engineering and operations research courses at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who utilize dynamic programming, stochastic programming, and control theory to solve problems in their everyday work.
Author :
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Page : 408 pages
File Size : 30,28 MB
Release : 1969
Category : Water resources development
ISBN :
Author : Stella Vosniadou
Publisher : Taylor & Francis
Page : 974 pages
File Size : 26,2 MB
Release : 2017-09-29
Category : Psychology
ISBN : 1317705564
This volume contains the invited lectures, invited symposia, symposia, papers and posters presented at the 2nd European Cognitive Science Conference held in Greece in May 2007. The papers presented in this volume range from empirical psychological studies and computational models to philosophical arguments, meta-analyses and even to neuroscientific experimentation. The quality of the work shows that the Cognitive Science Society in Europe is an exciting and vibrant one. There are 210 contributions by cognitive scientists from 27 different countries, including USA, France, UK, Germany, Greece, Italy, Belgium, Japan, Spain, the Netherlands, and Australia. This book will be of interest to anyone concerned with current research in Cognitive Science.
Author : Turab Lookman
Publisher : Springer
Page : 316 pages
File Size : 22,19 MB
Release : 2015-12-12
Category : Technology & Engineering
ISBN : 331923871X
This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a “fourth leg’’ to our toolkit to make the “Materials Genome'' a reality, the science of Materials Informatics.
Author : Jan Drugowitsch
Publisher : Springer Science & Business Media
Page : 274 pages
File Size : 32,76 MB
Release : 2008-05-30
Category : Computers
ISBN : 354079865X
This book is probably best summarized as providing a principled foundation for Learning Classi?er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de?nition – derived from machine learning – of “a good set of cl- si?ers”, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi?ers using that de?nition as a ?tness criterion, seeing ifthe setprovidesa goodsolutionto twodi?erent function approximation problems. It appears to, meaning that in some sense his de?nition of “good set of classi?ers” (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi?ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.
Author : Ralph G. Stahl, Jr.
Publisher : CRC Press
Page : 258 pages
File Size : 30,23 MB
Release : 2007-11-19
Category : Science
ISBN : 1420062638
Choosing the optimal management option requires environmental risk managers and decision makers to evaluate diverse, and not always congruent, needs and interests of multiple stakeholders. Understanding the trade-offs of different options as well as their legal, economic, scientific, and technological implications is critical to performing accurate
Author : David Insua
Publisher : John Wiley & Sons
Page : 315 pages
File Size : 17,58 MB
Release : 2012-04-02
Category : Mathematics
ISBN : 1118304039
Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.
