» » Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks

Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks epub

by Richard E. Neapolitan


Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks epub

ISBN: 0123704766

ISBN13: 978-0123704764

Author: Richard E. Neapolitan

Category: Technology

Subcategory: Networking & Cloud Computing

Language: English

Publisher: Morgan Kaufmann; 1 edition (April 17, 2009)

Pages: 424 pages

ePUB book: 1870 kb

FB2 book: 1115 kb

Rating: 4.5

Votes: 944

Other Formats: azw mbr txt docx





Richard E. Neapolitan. This book really helps in bridging formalism to understanding by providing lots of examples and walking through the examples. It's a pleasure to read. One can skim what seems basic. But if something is not clear, one can work through a few examples.

Richard E. It's strength is pedagogical. Neapolitan is professor and Chair of Computer Science at Northeastern Illinois University. He has previously written four books including the seminal 1990 Bayesian network text Probabilistic Reasoning in Expert Systems. Categories: Biology\Molecular: Bioinformatics.

Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics

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. The many useful applications of Bayesian networks that have been developed in the past.

with an Introduction to Bayesian Networks Richard E.

with an Introduction to Bayesian Networks. Authors: Richard E. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. Richard E.

Probabilistic Methods for Bioinformatics book.

Probabilistic Methods for Bioinformatics - Richard E. This book concentrates on bioinformatics, which applies the methods of informatics to solving problems in biology using biological data sets. Sometimes the terms bioinformatics and computational biology are used interchangeably. However, according to our definition, bioinformatics can be considered a subdiscipline of computational biology.

Probabilistic Methods for Bioinformatics. Book · January 2009 with 44 Reads. Bayesian Networks are a form of probabilistic graphical models and they are used for modeling knowledge in many application areas, from medicine to image processing

Probabilistic Methods for Bioinformatics. Isbn: 978-0123704764. Publisher: Morgan Kaufmann. Cite this publication. Richard E Neapolitan. Northeastern Illinois University. Bayesian Networks are a form of probabilistic graphical models and they are used for modeling knowledge in many application areas, from medicine to image processing. They are particularly useful for business applications, ans Unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance.

Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of. .

Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. However, there is no eort to be exhaustive in this discussion.

Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks. 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.

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.
Great book!
The material is very clearly explained. For many proofs the reader is referred to the author’s other book on the subject.
This book really helps in bridging formalism to understanding by providing lots of examples and walking through the examples. It's a pleasure to read.
One can skim what seems basic. But if something is not clear, one can work through a few examples. It's strength is pedagogical.