Taimre Thomas (53 results)
Solutions Manual to Accompany Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Kroese, Dirk P.; Taimre, Thomas; Botev, Zdravko I.; Rubinstein, Reuven Y.
Language: English
Published by Wiley-Interscience, 2007
Series: Book 209 of 358 - Wiley Series in Probability and Statistics
- Softcover
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paperback. Condition: Very Good. Unmarked trade paperback. Gentle bend on corner of cover.
Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Botev, Zdravko; Kroese, Dirk P.; Taimre, Thomas; Vaisman, Radislav
Language: English
Published by Chapman and Hall/CRC, 2019
Series: Book 15 of 15 - Chapman & Hall/Crc Machine Learning & Pattern Recognition
- Hardcover
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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Vaisman, Radislav,Taimre, Thomas,Botev, Zdravko I.,Kroese, Dirk P.
Language: English
Published by Chapman and Hall/CRC, 2019
Series: Book 15 of 15 - Chapman & Hall/Crc Machine Learning & Pattern Recognition
- Hardcover
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Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
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Simulation and the Monte Carlo Method
Kroese, Dirk P.; Taimre, Thomas; Botev, Zdravko I.; Rubinstein, Reuven Y.
Language: English
Published by Wiley-Interscience, 2007
Series: Book 209 of 358 - Wiley Series in Probability and Statistics
- Softcover
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Simulation and the Monte Carlo Method
Kroese, Dirk P.; Taimre, Thomas; Botev, Zdravko I.; Rubinstein, Reuven Y.
Language: English
Published by Wiley-Interscience, 2007
Series: Book 209 of 358 - Wiley Series in Probability and Statistics
- Softcover
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Solutions Manual to Accompany Simulation and the Monte Carlo Method
Kroese Dirk P. Rubinstein Reuven Y. Botev Zdravko I. Taimre Thomas
Language: English
Published by John Wiley & Sons, 2007
Series: Book 209 of 358 - Wiley Series in Probability and Statistics
- Softcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Condition: New. pp. 188.
- Hardcover
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Simulation and the Monte Carlo Method
Kroese, Dirk P.; Taimre, Thomas; Botev, Zdravko I.; Rubinstein, Reuven Y.
Language: English
Published by Wiley-Interscience, 2007
Series: Book 209 of 358 - Wiley Series in Probability and Statistics
- Softcover
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Solutions Manual to Accompany Simulation and the Monte Carlo Method
Dirk P. Kroese Reuven Y. Rubinstein Zdravko I. Botev Thomas Taimre
Language: English
Published by John Wiley & Sons, 2007
Series: Book 209 of 358 - Wiley Series in Probability and Statistics
- Softcover
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Condition: New. pp. 188 2nd Edition.
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Simulation and the Monte Carlo Method
Kroese, Dirk P.; Taimre, Thomas; Botev, Zdravko I.; Rubinstein, Reuven Y.
Language: English
Published by Wiley-Interscience, 2007
Series: Book 209 of 358 - Wiley Series in Probability and Statistics
- Softcover
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Language: English
Published by Wiley Dez 2007, 2007
Series: Book 209 of 358 - Wiley Series in Probability and Statistics
- Softcover
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Taschenbuch. Condition: Neu. Neuware - This accessible new edition explores the major topics in Monte Carlo simulationSimulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simu…lation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences.The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including:Markov Chain Monte CarloVariance reduction techniques such as the transform likelihood ratio method and the screening methodThe score function method for sensitivity analysisThe stochastic approximation method and the stochastic counter-part method for Monte Carlo optimizationThe cross-entropy method to rare events estimation and combinatorial optimizationApplication of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy methodAn extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB(r) programs.Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.
Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Botev, Zdravko Zdravko Botev, Dirk P. Kroese, Thomas Taimre,
- Hardcover
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- Hardcover
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Condition: acceptable. Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadab…le. Any access codes or passwords originally included with the book may be expired, used or no longer valid. Image is stock photo and cover art edition may be different than pictured.
- Hardcover
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- Softcover
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- Hardcover
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Hardback. Condition: New. 2nd. Praise for the first edition:"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science."- Joacim Rocklöv and Albert A. Gayle, Internat…ional Journal of Epidemiology, Volume 49, Issue 6"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely-very useful for readers who wish to understand the rationale and flow of the background knowledge."- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization-Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.
Data Science and Machine Learning : Mathematical and Statistical Methods, 1st Edition
Botev, Zdravko; Kroese, Dirk P.; Taimre, Thomas; Vaisman, Radislav
Language: English
Published by Taylor & Francis Group, 2019
Series: Book 15 of 15 - Chapman & Hall/Crc Machine Learning & Pattern Recognition
- Hardcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Hardback. Condition: New. 2nd. Praise for the first edition:"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science."- Joacim Rocklöv and Albert A. Gayle, Internat…ional Journal of Epidemiology, Volume 49, Issue 6"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely-very useful for readers who wish to understand the rationale and flow of the background knowledge."- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization-Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.
- Softcover
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Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Data Science and Machine Learning
Zdravko Botev (University of New South Wales)|Dirk P. Kroese|Thomas Taimre
- Hardcover
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Condition: New. Zdravko I. Botev, PhD, is the pioneer of several modern statistical methodologies, including the diffusion kernel density estimator, the generalized splitting method for rare-event simulation, the bandwidth perturbation matching.
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