Handbook of Monte Carlo Methods 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Handbook of Monte Carlo Methods

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D.P. Kroese 作者
Wiley
譯者
2011-3-15 出版日期
772 頁數
USD 145.00 價格
Hardcover
叢書系列
9780470177938 圖書編碼

Handbook of Monte Carlo Methods 在線電子書 圖書標籤: 濛特卡羅  simulation  Matlab  statistics  機器學習  數學-概率統計  數學  Statistics   


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Handbook of Monte Carlo Methods 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Handbook of Monte Carlo Methods 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Handbook of Monte Carlo Methods 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



Handbook of Monte Carlo Methods 在線電子書 用戶評價

評分

重點介紹模擬計算中的濛特卡羅法,基本上每個算法都給齣瞭相應的用例和matlab代碼。可惜其中有幾章感腳就是在堆論文。。。

評分

比較少見的用Matlab介紹具體實現的書,參考起來不錯

評分

重點介紹模擬計算中的濛特卡羅法,基本上每個算法都給齣瞭相應的用例和matlab代碼。可惜其中有幾章感腳就是在堆論文。。。

評分

重點介紹模擬計算中的濛特卡羅法,基本上每個算法都給齣瞭相應的用例和matlab代碼。可惜其中有幾章感腳就是在堆論文。。。

評分

比較少見的用Matlab介紹具體實現的書,參考起來不錯

Handbook of Monte Carlo Methods 在線電子書 著者簡介


Handbook of Monte Carlo Methods 在線電子書 著者簡介


Handbook of Monte Carlo Methods 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Handbook of Monte Carlo Methods 在線電子書 圖書描述

Product Description

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications

More and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field.

The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including:

Random variable and stochastic process generation

Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run

Discrete-event simulation

Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation

Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo

Estimation of derivatives and sensitivity analysis

Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization

The presented theoretical concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation.

Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.

From the Back Cover

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications

More and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that facilitate a thorough understanding of the emerging dynamics of this rapidly growing field.

The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including:

Random variable and stochastic process generation

Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run

Discrete-event simulation

Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation

Variance reduction, including importance sampling, Latin hypercube sampling, and conditional Monte Carlo

Estimation or derivatives and sensitivity analysis

Advanced topics including cross-entropy, rare events, kernel density estimation, quasi-Monte Carlo, particle systems, and randomized optimization

The presented theoretical concepts are illustrated with worked examples that use MATLAB®. A related website houses the MATLAB® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that ate relevant to Monte Carlo simulation.

Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics as the upper-undergraduate and graduate levels.

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