Multiscale Modeling: A Bayesian Perspective
Multiscale Modeling: A Bayesian Perspective
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发表时间：
2007-07
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通讯作者：
Marco A. R. Ferreira;Herbert K. H. Lee
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作者：
Marco A. R. Ferreira;Herbert K. H. Lee
各种各样的过程在多个尺度上发生，要么是自然发生的，要么是测量的结果。本书包含了分析此类多尺度过程中产生的数据的方法。这本书汇集了许多最新进展，并让更广泛的读者能够接触到它们。采用贝叶斯方法可以充分考虑不确定性，并解决多个尺度的不确定性的微妙问题。贝叶斯方法还有助于使用来自先前经验或数据的知识，并且这些方法可以处理不同规模的不同数量的先验知识，正如实践中经常发生的那样。本书的目标读者是统计学家、应用数学家和工程师，他们致力于处理时间和/或空间中的多尺度过程问题，例如工程、金融和环境计量学领域的问题。从事多尺度计算研究的人们也会对这本书感兴趣。主要先决条件是贝叶斯统计知识和基本马尔可夫链蒙特卡罗方法。为了演示这些方法并帮助读者将这些方法应用到自己的工作中，对许多现实世界的例子进行了彻底的分析。为了进一步帮助读者，作者正在为本文讨论的许多基本方法提供源代码（R）。
A wide variety of processes occur on multiple scales, either naturally or as a consequence of measurement. This book contains methodology for the analysis of data that arise from such multiscale processes. The book brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. The Bayesian approach also facilitates the use of knowledge from prior experience or data, and these methods can handle different amounts of prior knowledge at different scales, as often occurs in practice. The book is aimed at statisticians, applied mathematicians, and engineers working on problems dealing with multiscale processes in time and/or space, such as in engineering, finance, and environmetrics. The book will also be of interest to those working on multiscale computation research. The main prerequisites are knowledge of Bayesian statistics and basic Markov chain Monte Carlo methods. A number of real-world examples are thoroughly analyzed in order to demonstrate the methods and to assist the readers in applying these methods to their own work. To further assist readers, the authors are making source code (for R) available for many of the basic methods discussed herein.