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Bandit Algorithms for Website Optimization
15,99 € *
ggf. zzgl. Versand

Bandit Algorithms for Website Optimization ab 15.99 EURO

Anbieter: ebook.de
Stand: 23.01.2020
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Multi-armed Bandit Allocation Indices
152,00 CHF *
ggf. zzgl. Versand

Multi-armed Bandit Allocation Indices: 2nd Edition John Gittins, Statistics Department, University of Oxford, UK Kevin Glazebrook, Department of Management Science, Lancaster University, UK Richard Weber, Statistical Laboratory, University of Cambridge, UK In 1989 the first edition of this book set out Gittins' pioneering index solution to the multi-armed bandit problem and his subsequent investigation of a wide class of sequential resource allocation and stochastic scheduling problems. Since then there has been a remarkable flowering of new insights, generalizations and applications, to which Glazebrook and Weber have made major contributions. This second edition brings the story up to date. There are new chapters on the achievable region approach to stochastic optimization problems, the construction of performance bounds for suboptimal policies, Whittle's restless bandits, and the use of Lagrangian relaxation in the construction and evaluation of index policies. Some of the many varied proofs of the index theorem are discussed along with the insights that they provide. Many contemporary applications are surveyed, and over 150 new references are included. Over the past 40 years the Gittins index has helped theoreticians and practitioners to address a huge variety of problems within chemometrics, economics, engineering, numerical analysis, operational research, probability, statistics and website design. This new edition will be an important resource for others wishing to use this approach.

Anbieter: Orell Fuessli CH
Stand: 23.01.2020
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Optimal Learning
259,00 CHF *
ggf. zzgl. Versand

Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: * Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems * Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems * Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduc tion to learning and a variety of policies for learning.

Anbieter: Orell Fuessli CH
Stand: 23.01.2020
Zum Angebot
Bandit Algorithms for Website Optimization
19,90 CHF *
zzgl. 3,50 CHF Versand

When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success. This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website. * Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithms * Develop a unit testing framework for debugging bandit algorithms * Get additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials

Anbieter: Orell Fuessli CH
Stand: 23.01.2020
Zum Angebot
Optimal Learning
105,00 CHF *
ggf. zzgl. Versand

Learn the science of collecting information to make effectivedecisions Everyday decisions are made without the benefit of accurateinformation. Optimal Learning develops the needed principlesfor gathering information to make decisions, especially whencollecting information is time-consuming and expensive. Designedfor readers with an elementary background in probability andstatistics, the book presents effective and practical policiesillustrated in a wide range of applications, from energy, homelandsecurity, and transportation to engineering, health, andbusiness. This book covers the fundamental dimensions of a learningproblem and presents a simple method for testing and comparingpolicies for learning. Special attention is given to the knowledgegradient policy and its use with a wide range of belief models,including lookup table and parametric and for online and offlineproblems. Three sections develop ideas with increasing levels ofsophistication: * Fundamentals explores fundamental topics, includingadaptive learning, ranking and selection, the knowledge gradient,and bandit problems * Extensions and Applications features coverage of linearbelief models, subset selection models, scalar functionoptimization, optimal bidding, and stopping problems * Advanced Topics explores complex methods includingsimulation optimization, active learning in mathematicalprogramming, and optimal continuous measurements Each chapter identifies a specific learning problem, presentsthe related, practical algorithms for implementation, and concludeswith numerous exercises. A related website features additionalapplications and downloadable software, including MATLAB and theOptimal Learning Calculator, a spreadsheet-based package thatprovides an introduc-tion to learning and a variety ofpolicies for learning.

Anbieter: Orell Fuessli CH
Stand: 23.01.2020
Zum Angebot
Multi-armed Bandit Allocation Indices
90,00 CHF *
ggf. zzgl. Versand

In 1989 the first edition of this book set out Gittins' pioneering index solution to the multi-armed bandit problem and his subsequent investigation of a wide of sequential resource allocation and stochastic scheduling problems. Since then there has been a remarkable flowering of new insights, generalizations and applications, to which Glazebrook and Weber have made major contributions. This second edition brings the story up to date. There are new chapters on the achievable region approach to stochastic optimization problems, the construction of performance bounds for suboptimal policies, Whittle's restless bandits, and the use of Lagrangian relaxation in the construction and evaluation of index policies. Some of the many varied proofs of the index theorem are discussed along with the insights that they provide. Many contemporary applications are surveyed, and over 150 new references are included. Over the past 40 years the Gittins index has helped theoreticians and practitioners to address a huge variety of problems within chemometrics, economics, engineering, numerical analysis, operational research, probability, statistics and website design. This new edition will be an important resource for others wishing to use this approach.

Anbieter: Orell Fuessli CH
Stand: 23.01.2020
Zum Angebot
Optimal Learning
110,00 CHF *
ggf. zzgl. Versand

Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduc tion to learning and a variety of policies for learning.

Anbieter: Orell Fuessli CH
Stand: 23.01.2020
Zum Angebot
Optimal Learning
151,99 € *
ggf. zzgl. Versand

Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: * Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems * Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems * Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduc tion to learning and a variety of policies for learning.

Anbieter: Thalia AT
Stand: 23.01.2020
Zum Angebot
Multi-armed Bandit Allocation Indices
109,99 € *
ggf. zzgl. Versand

Multi-armed Bandit Allocation Indices: 2nd Edition John Gittins, Statistics Department, University of Oxford, UK Kevin Glazebrook, Department of Management Science, Lancaster University, UK Richard Weber, Statistical Laboratory, University of Cambridge, UK In 1989 the first edition of this book set out Gittins' pioneering index solution to the multi-armed bandit problem and his subsequent investigation of a wide class of sequential resource allocation and stochastic scheduling problems. Since then there has been a remarkable flowering of new insights, generalizations and applications, to which Glazebrook and Weber have made major contributions. This second edition brings the story up to date. There are new chapters on the achievable region approach to stochastic optimization problems, the construction of performance bounds for suboptimal policies, Whittle's restless bandits, and the use of Lagrangian relaxation in the construction and evaluation of index policies. Some of the many varied proofs of the index theorem are discussed along with the insights that they provide. Many contemporary applications are surveyed, and over 150 new references are included. Over the past 40 years the Gittins index has helped theoreticians and practitioners to address a huge variety of problems within chemometrics, economics, engineering, numerical analysis, operational research, probability, statistics and website design. This new edition will be an important resource for others wishing to use this approach.

Anbieter: Thalia AT
Stand: 23.01.2020
Zum Angebot