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Sprache: Englisch
Verlag: New York/Heidelberg, Springer (Undergraduate Texts in Mathematics / UTM), 2009
ISBN 10: 038787836X ISBN 13: 9780387878362
Anbieter: Antiquariat Smock, Freiburg, Deutschland
Erstausgabe
Zustand: Sehr gut. Formateinband: illustrierter Pappband / gebundene Ausgabe XII, 243 S. 1st Edition; Sehr guter Zustand. Sprache: Englisch Gewicht in Gramm: 650 [Stichwörter: Monte Carlo-Methoden, Finance with Monte Carlo, Introduction, Some probability distributions and their uses, Markov Chain Monte Carlo, Optimization by Monte Carlo Methods, Random Walks, Generating uniform random numbers, Perron-Frobenius Theorem].
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Sprache: Englisch
Verlag: Springer-Verlag New York Inc., US, 2009
ISBN 10: 038787836X ISBN 13: 9780387878362
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Sprache: Englisch
Verlag: Springer International Publishing AG, CH, 2024
ISBN 10: 3031559630 ISBN 13: 9783031559631
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Sprache: Englisch
Verlag: Springer, Berlin|Springer Nature Switzerland|Springer, 2024
ISBN 10: 3031559630 ISBN 13: 9783031559631
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Sprache: Englisch
Verlag: Springer Nature Switzerland, Springer Nature Switzerland Jun 2024, 2024
ISBN 10: 3031559630 ISBN 13: 9783031559631
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Buch. Zustand: Neu. Neuware -Monte Carlo Methods are among the most used, and useful, computational tools available today. They provide efficient and practical algorithms to solve a wide range of scientific and engineering problems in dozens of areas many of which are covered in this text. These include simulation, optimization, finance, statistical mechanics, birth and death processes, Bayesian inference, quadrature, gambling systems and more.This text is for students of engineering, science, economics and mathematics who want to learn about Monte Carlo methods but have only a passing acquaintance with probability theory. The probability needed to understand the material is developed within the text itself in a direct manner using Monte Carlo experiments for reinforcement. There is a prerequisite of at least one year of calculus and a semester of matrix algebra.Each new idea is carefully motivated by a realistic problem, thus leading to insights into probability theory via examples and numerical simulations. Programming exercises are integrated throughout the text as the primary vehicle for learning the material. All examples in the text are coded in Python as a representative language; the logic is sufficiently clear so as to be easily translated into any other language. Further, Python scripts for each worked example are freely accessible for each chapter. Along the way, most of the basic theory of probability is developed in order to illuminate the solutions to the questions posed. One of the strongest features of the book is the wealth of completely solved example problems. These provide the reader with a sourcebook to follow towards the solution of their own computational problems. Each chapter ends with a large collection of homework problems illustrating and directing the material. This book is suitable as a textbook for students of engineering, finance, and the sciences as well as mathematics. The problem-oriented approach makes it ideal for an applied course in basic probability as well as for a more specialized course in Monte Carlo Methods. Topics include probability distributions, probability calculations, sampling, counting combinatorial objects, Markov chains, random walks, simulated annealing, genetic algorithms, option pricing, gamblers ruin, statistical mechanics, random number generation, Bayesian Inference, Gibbs Sampling and Monte Carlo integration.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch.
Sprache: Englisch
Verlag: Springer-Verlag New York Inc., US, 2009
ISBN 10: 038787836X ISBN 13: 9780387878362
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In den WarenkorbHardback. Zustand: New. 2009 ed.
Sprache: Englisch
Verlag: Springer Nature Switzerland, 2024
ISBN 10: 3031559630 ISBN 13: 9783031559631
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Monte Carlo Methods are among the most used, and useful, computationaltools available today. They provide efficient and practical algorithms to solve a wide range of scientific and engineering problems in dozens of areas many of which are covered in this text. These include simulation, optimization, finance, statistical mechanics, birth and death processes, Bayesian inference, quadrature, gambling systems and more.This text is for students of engineering, science, economics andmathematics who want to learn about Monte Carlo methods but haveonly a passing acquaintance with probability theory. The probability needed to understand the material is developed within the text itself in a direct manner using Monte Carlo experiments for reinforcement. There is a prerequisite of at least one year of calculus and a semester of matrix algebra.Each new idea is carefully motivated by a realistic problem, thusleading to insights into probability theory via examples and numericalsimulations. Programming exercises are integrated throughout the text as the primary vehicle for learning the material. All examples in the text are coded in Python as a representative language; the logic is sufficiently clear so as to be easily translated into any other language. Further, Python scripts for each worked example are freely accessible for each chapter. Along the way, most of the basic theory of probability is developed in order to illuminate the solutions to the questions posed. One of the strongest features of the book is the wealth of completely solved example problems. These provide the reader with a sourcebook to follow towards the solution of their own computational problems.Each chapter ends with a large collection of homework problems illustratingand directing the material.This book is suitable as a textbook for students of engineering, finance,and the sciences as well as mathematics. The problem-oriented approach makes it ideal for an applied course in basic probability as well as for a more specialized course in Monte Carlo Methods. Topics include probability distributions, probability calculations, sampling, counting combinatorial objects, Markov chains, random walks, simulated annealing, genetic algorithms, option pricing, gamblers ruin, statistical mechanics, random number generation, Bayesian Inference, Gibbs Samplingand Monte Carlo integration.
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In den WarenkorbPaperback. Zustand: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Verlag: Springer
Anbieter: Academic Book Solutions, Medford, NY, USA
hardcover. Zustand: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.