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 modelling, 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.
Rezensionen / Stimmen
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)
