TY  - BOOK
AU  - Husmeier,Dirk
AU  - Dybowski,Richard
AU  - Roberts,Stephen
ED  - SpringerLink (Online service)
TI  - Probabilistic Modeling in Bioinformatics and Medical Informatics
T2  - Advanced Information and Knowledge Processing
SN  - 9781846281198
AV  - QA276-280 
U1  - 005.55 23
PY  - 2005///
CY  - London
PB  - Springer London
KW  - Computer science
KW  - Health informatics
KW  - Algorithms
KW  - Mathematical statistics
KW  - Mathematics
KW  - Bioinformatics
KW  - Statistics
KW  - Computer Science
KW  - Probability and Statistics in Computer Science
KW  - Math Applications in Computer Science
KW  - Algorithm Analysis and Problem Complexity
KW  - Statistics for Life Sciences, Medicine, Health Sciences
KW  - Health Informatics
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/b138794
ER  -