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Bayesian Reasoning and Machine Learning
We live in a world that is rich in data, ever increasing in scale. This data comes from many di erent
sources in science (bioinformatics, astronomy, physics, environmental monitoring) and commerce (customer
databases, nancial transactions, engine monitoring, speech recognition, surveillance, search). Possessing
the knowledge as to... | | Epigenetics
The regulation of gene expression in many biological processes involves epigenetic mechanisms. In this new volume, 24 chapters written by experts in the field discuss epigenetic effects from many perspectives. There are chapters on the basic molecular mechanisms underpinning epigenetic regulation, discussion of cellular processes that rely on... | | Information Theory and Statistical Learning
This book presents theoretical and practical results of information theoretic methods
used in the context of statistical learning. Its major goal is to advocate and promote
the importance and usefulness of information theoretic concepts for understanding
and developing the sophisticated machine learning methods necessary not only... |
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| | Prediction of Protein Structures, Functions, and Interactions
The growing flood of new experimental data generated by genome sequencing has provided an impetus for the development of automated methods for predicting the functions of proteins that have been deduced by sequence analysis and lack experimental characterization.
Prediction of Protein Structures, Functions and Interactions presents... | | Cluster Analysis for Data Mining and System Identification
Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets... |
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