马尔可夫链：从理论到实施和实验（真/零售EPUB）

English | July 5th, 2017 | ISBN: 1119387558 | 256 pages | True EPUB | 9.15 MB

A fascinating and instructive guide to Markov chains for experienced users and newcomers alike

This unique guide to Markov chains approaches the subject along the four convergent lines of mathematics, implementation, simulation, and experimentation. It introduces readers to the art of stochastic modeling, shows how to design computer implementations, and provides extensive worked examples with case studies.

Markov Chains: From Theory to Implementation and Experimentation begins with a general introduction to the history of probability theory in which the author uses quantifiable examples to illustrate how probability theory arrived at the concept of discrete-time and the Markov model from experiments involving independent variables. An introduction to simple stochastic matrices and transition probabilities is followed by a simulation of a two-state Markov chain. The notion of steady state is explored in connection with the long-run distribution behavior of the Markov chain. Predictions based on Markov chains with more than two states are examined, followed by a discussion of the notion of absorbing Markov chains. Also covered in detail are topics relating to the average time spent in a state, various chain configurations, and n-state Markov chain simulations used for verifying experiments involving various diagram configurations.

• Fascinating historical notes shed light on the key ideas that led to the development of the Markov model and its variants • Various configurations of Markov Chains and their limitations are explored at length • Numerous examples—from basic to complex—are presented in a comparative manner using a variety of color graphics • All algorithms presented can be analyzed in either Visual Basic, Java Script, or PHP • Designed to be useful to professional statisticians as well as readers without extensive knowledge of probability theory

Covering both the theory underlying the Markov model and an array of Markov chain implementations, within a common conceptual framework, Markov Chains: From Theory to Implementation and Experimentation is a stimulating introduction to and a valuable reference for those wishing to deepen their understanding of this extremely valuable statistical tool.

Paul A. Gagniuc, PhD, is Associate Professor at Polytechnic University of Bucharest, Romania. He obtained his MS and his PhD in genetics at the University of Bucharest. Dr. Gagniuc's work has been published in numerous high profile scientific journals, ranging from the Public Library of Science to BioMed Central and Nature journals. He is the recipient of several awards for exceptional scientific results and a highly active figure in the review process for different scientific areas.

中文|2017年7月5日|ISBN:1119387558|256页|True EPUB | 9.15 MB一本有趣且有指导意义的马尔可夫链指南，适合经验丰富的用户和新手 这本关于马尔可夫链的独特指南沿着数学、实现、模拟和实验四条收敛线来探讨这一主题。它向读者介绍了随机建模的艺术，展示了如何设计计算机实现，并提供了大量的案例研究实例。 《马尔可夫链：从理论到实现和实验》首先对概率论的历史进行了总体介绍，作者使用可量化的例子来说明概率论是如何从涉及自变量的实验中得出离散时间和马尔可夫模型的概念的。在介绍了简单的随机矩阵和转移概率之后，对双态马尔可夫链进行了模拟。结合马尔可夫链的长期分布行为，探讨了稳态的概念。研究了基于具有两个以上状态的马尔可夫链的预测，然后讨论了吸收马尔可夫链的概念。还详细介绍了与在一个状态中花费的平均时间、各种链配置以及用于验证涉及各种图配置的实验的n态马尔可夫链模拟相关的主题。 •引人入胜的历史笔记揭示了导致马尔可夫模型及其变体发展的关键思想•详细探讨了马尔可夫链的各种配置及其局限性•使用各种彩色图形以比较的方式呈现了从基本到复杂的许多示例•所呈现的所有算法都可以用Visual basic、Java Script或PHP进行分析•旨在对专业统计学家以及没有广泛概率论知识的读者有用 《马尔可夫链：从理论到实现和实验》在一个共同的概念框架内涵盖了马尔可夫模型的基础理论和一系列马尔可夫链实现，对于那些希望加深对这一极其有价值的统计工具的理解的人来说，这是一个令人兴奋的介绍和有价值的参考。 Paul A.Gagniuc，博士，罗马尼亚布加勒斯特理工大学副教授。他在布加勒斯特大学获得了遗传学硕士和博士学位。Gagniuc博士的研究发表在众多知名科学期刊上，从《公共科学图书馆》到《生物医学中心》和《自然》杂志。他因卓越的科学成果获得了多项奖项，并在不同科学领域的审查过程中发挥了积极作用。

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