Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly.

Probabilistic Modeling.- A Leisurely Look at Statistical Inference.- to Learning Bayesian Networks from Data.- A Casual View of Multi-Layer Perceptrons as Probability Models.- Bioinformatics.- 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 Profiling 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 Marginalization.- 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.

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)

There is currently no other book that provides a contemporary survey of probabilistic models in both bio-medicine and bio-informatics An internet site will accompany the book Includes supplementary material: sn.pub/extras