000 03773nam a22005895i 4500
001 978-1-84628-119-8
003 DE-He213
005 20160302161529.0
007 cr nn 008mamaa
008 100301s2005 xxk| s |||| 0|eng d
020 _a9781846281198
_9978-1-84628-119-8
024 7 _a10.1007/b138794
_2doi
050 4 _aQA276-280
072 7 _aUYAM
_2bicssc
072 7 _aUFM
_2bicssc
072 7 _aCOM077000
_2bisacsh
082 0 4 _a005.55
_223
245 1 0 _aProbabilistic Modeling in Bioinformatics and Medical Informatics
_h[electronic resource] /
_cedited by Dirk Husmeier, Richard Dybowski, Stephen Roberts.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _aXX, 508 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Information and Knowledge Processing
505 0 _aProbabilistic 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.
520 _aProbabilistic 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.
650 0 _aComputer science.
650 0 _aHealth informatics.
650 0 _aAlgorithms.
650 0 _aMathematical statistics.
650 0 _aComputer science
_xMathematics.
650 0 _aBioinformatics.
650 0 _aStatistics.
650 1 4 _aComputer Science.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aMath Applications in Computer Science.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
650 2 4 _aBioinformatics.
650 2 4 _aHealth Informatics.
700 1 _aHusmeier, Dirk.
_eeditor.
700 1 _aDybowski, Richard.
_eeditor.
700 1 _aRoberts, Stephen.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781852337780
830 0 _aAdvanced Information and Knowledge Processing
856 4 0 _uhttp://dx.doi.org/10.1007/b138794
912 _aZDB-2-SCS
999 _c173644
_d173644