Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks (Hardcover)

The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics.

Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis.

• Unique coverage of probabilistic reasoning methods applied to bioinformatics data--those methods that are likely to become the standard analysis tools for bioinformatics.


• Shares insights about when and why probabilistic methods can and cannot be used effectively;


• Complete review of Bayesian networks and probabilistic methods with a practical approach.
  

貝葉斯網絡是表示和推理多變量概率分佈的最重要架構之一。當與專業的信息學結合使用時，可以實現真實世界應用的可能性。《生物信息學的概率方法》解釋了概率和統計，特別是貝葉斯網絡在遺傳學中的應用。本書提供了有關概率、統計和遺傳學的背景材料，然後討論了貝葉斯網絡及其在生物信息學中的應用。

本書以應用和案例研究的方式，以易於理解的方式解釋了用於生物信息學數據的概率方法和貝葉斯網絡，而不是陷入證明和算法的細節。本書討論了過去10年中開發的許多有用的貝葉斯網絡應用。這是對該領域所有重要工作的綜述，可以說將成為生物數據分析中最普遍的方法。

本書的特點包括：
- 獨特的覆蓋範圍，涵蓋了應用於生物信息學數據的概率推理方法，這些方法可能成為生物信息學的標準分析工具。
- 分享了何時以及為什麼概率方法可以有效使用的見解。
- 全面回顧了貝葉斯網絡和概率方法，並以實用的方式進行了介紹。