Probabilistic Modeling in Bioinformatics and Medical Informatics

Download or Read online Probabilistic Modeling in Bioinformatics and Medical Informatics full in PDF, ePub and kindle. This book written by Dirk Husmeier and published by Springer Science & Business Media which was released on 30 March 2006 with total pages 508. We cannot guarantee that Probabilistic Modeling in Bioinformatics and Medical Informatics book is available in the library, click Get Book button to download or read online books. Join over 650.000 happy Readers and READ as many books as you like.

Probabilistic Modeling in Bioinformatics and Medical Informatics
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Publisher : Springer Science & Business Media
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ISBN : 9781846281198
Pages : 508 pages
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Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.

Probabilistic Modeling in Bioinformatics and Medical Informatics

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and

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Probabilistic Modeling in Bioinformatics and Medical Informatics

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and

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