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Details: This book is printed on demand. (allow 1-2 weeks for printing)(424 pages) the bayesian *n*e*t*work 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 *n*e*t*works, to ge*n*e*t*ics. this book provides background material on probability, statistics, and ge*n*e*t*ics, and then moves on to discuss bayesian *n*e*t*works and applications to bioinformatics. rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and bayesian *n*e*t*works are explained in an accessible way using applications and case studies. the many useful applications of bayesian *n*e*t*works 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 *n*e*t*works and probabilistic methods with a practical approach. i background chapter 1: probabilistic informatics chapter 2: probability basics chapter 3: statistics basics chapter 4: ge*n*e*t*ics basics ii bayesian *n*e*t*works chapter 5: foundations of bayesian *n*e*t*works chapter 6: further properties of bayesian *n*e*t*works chapter 7: learning bayesian *n*e*t*work parameters chapter 8: learning bayesian *n*e*t*work structure iii bioinformatics applications chapter 9: nonmolecular evolutionary ge*n*e*t*ics chapter 10: molecular evolutionary ge*n*e*t*ics chapter 11: molecular phyloge*n*e*t*ics chapter 12: analyzing gene expression data chapter 13: ge*n*e*t*ic-linkage analysis biographicalnote: richard e. neapolitan is professor and chair of computer science at northeastern illinois university. he has previously written four books including the seminal 1990 bayesian *n*e*t*work text probabilistic reasoning in expert systems. more recently, he wrote the 2004 text learning bayesian *n*e*t*works, the textbook foundations of algorithms, which has been translated to three languages and is one of the most widely-used algorithms texts world-wide, and the 2007 text probabilistic methods for financial and marketing informatics (morgan kaufmann publishers). audiencedescription: this book is for all randd professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to biological information. this includes computer science and other professionals in the data management and data mining field whose interests are bioinformatics in general, and who want to apply ai and probabilistic methods to their problems--in order to better make predictions about the data. for instance, suppose you have long homologous dna sequences from the human, the chimpanzee, the gorilla, the orangutan, and the rhesus monkey. one can use the methologies from informatics to obtain new information about which species is most closely related to the human. (Hardback)

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Ships from United Kingdom* (View Shipping Times and Rates)

Details: The bayesian *n*e*t*work is one of the most important architectures for representing and reasoning with multivariate probability distributions. this book provides background material on probability, statistics, and ge*n*e*t*ics, and then moves on to discuss bayesian *n*e*t*works and applications to bioinformatics. approx. 150 illustrations (Hardback)

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Details: New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the UK.

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Details: Sewn binding. Paper over boards. 406 p. Contains: Illustrations.

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