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Probabilistic Modeling in Bioinformatics and Medical Informatics : Advanced Information and Knowledge Processing - Dirk Husmeier

Probabilistic Modeling in Bioinformatics and Medical Informatics

Advanced Information and Knowledge Processing

By: Dirk Husmeier (Editor), Richard Dybowski (Editor), Stephen Roberts (Editor)

Hardcover

Published: 1st February 2005
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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.

From the reviews:

"This book is a collection of chapters describing methods of statistical analysis of medical and biological data, with a focus on mathematical descriptions and implementing algorithms. ... It will be particularly useful for those who are interested in a better understanding of artificial neutral networks ... . Generally, it is a refreshing book for a statistician ... giving a good description of a wide variety of complex models." (Natalia Bochkina, Significance, Vol. 3 (3), 2006)

"This book covers recent advances in the use of probabilistic models in computational molecular biology, bioinformatics and biomedicine. ... A self-contained chapter on statistical inference is included as well as a discussion of Bayesian networks as a common and unifying framework for probabilistic modeling. The book has been written for researchers and students in statistics, informatics, and biological sciences ... . Finally, an appendix explains the conventions and notation used throughout the book." (T. Postelnicu, Zentralblatt MATH, Vol. 1151, 2009)

Probabilistic Modelling
A Leisurely Look at Statistical Inference
Introduction to Learning Bayesian Networks from Data
A Casual View of Multi-Layer Perceptrons as Probability Models
Bioinformatics
Introduction to Statistical Phylogenetics
Detecting Recombination in DNA Sequence Alignments
RNA-Based Phylogenetic Methods
Statistical Methods in Microarray Gene Expression Data Analysis
Inferring Genetic Regulatory Networks from Microarray Experiments with Bayesian Networks
Modeling Genetic Regulatory Networks using Gene Expression Profling and State Space Models
Medical Informatics
An Anthology of Probabilistic Models for Medical Informatics
Bayesian Analysis of Population Pharmacokinetic/Pharmacodynamic Models
Assessing the Effectiveness of Bayesian Feature Selection
Bayes Consistent Classification of EEG Data by Approximate Marginalisation
Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis
A Probabilistic Network for Fusion of Data and Knowledge in Clinical Microbiology
Software for Probability Models in Medical Informatics A Conventions and Notation
Index
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9781852337780
ISBN-10: 1852337788
Series: Advanced Information and Knowledge Processing
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 508
Published: 1st February 2005
Publisher: Springer London Ltd
Country of Publication: GB
Dimensions (cm): 23.5 x 15.5  x 3.18
Weight (kg): 2.01