Handbook of Monte Carlo Methods 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Handbook of Monte Carlo Methods

简体网页||繁体网页
D.P. Kroese 作者
Wiley
译者
2011-3-15 出版日期
772 页数
USD 145.00 价格
Hardcover
丛书系列
9780470177938 图书编码

Handbook of Monte Carlo Methods 在线电子书 图书标签: 蒙特卡罗  simulation  Matlab  statistics  机器学习  数学-概率统计  数学  Statistics   


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Handbook of Monte Carlo Methods 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Handbook of Monte Carlo Methods 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Handbook of Monte Carlo Methods 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



Handbook of Monte Carlo Methods 在线电子书 用户评价

评分

重点介绍模拟计算中的蒙特卡罗法,基本上每个算法都给出了相应的用例和matlab代码。可惜其中有几章感脚就是在堆论文。。。

评分

重点介绍模拟计算中的蒙特卡罗法,基本上每个算法都给出了相应的用例和matlab代码。可惜其中有几章感脚就是在堆论文。。。

评分

比较少见的用Matlab介绍具体实现的书,参考起来不错

评分

重点介绍模拟计算中的蒙特卡罗法,基本上每个算法都给出了相应的用例和matlab代码。可惜其中有几章感脚就是在堆论文。。。

评分

比较少见的用Matlab介绍具体实现的书,参考起来不错

Handbook of Monte Carlo Methods 在线电子书 著者简介


Handbook of Monte Carlo Methods 在线电子书 图书目录


Handbook of Monte Carlo Methods 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Handbook of Monte Carlo Methods 在线电子书 图书描述

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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