Author:
Edition: 2012
Publisher: Springer
Binding: Hardcover
ISBN: 364227224X
Bayesian Methods in Structural Bioinformatics (Statistics for Biology and Health)
This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). Medical books Bayesian Methods in Structural Bioinformatics . It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics Medical books Bayesian Methods in Structural Bioinformatics. This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.
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This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is h
format hardback language english publication year 26 03 2012 series statistics for biology and health subject mathematics sciences subject 2 science mathematics textbooks study guides title bayesian methods in structural bioinformatics author hamelryck thomas editor mardia kanti editor ferkinghoff borg jesper editor publisher springer verlag publication date mar 24 2012 pages 385 binding hardcover edition 1 st dimensions 6 00 wx 9 25 hx 0 75 d isbn 364227224 x subject medical biostatistics des
Store Search search Title, ISBN and Author Bayesian Methods in Structural Bioinformatics Estimated delivery 3-12 business days Format Hardcover Condition Brand New This edited volume is a high-profile overview of the current state of play in statistical methods applied to structural bioinformatics. With almost 100 pages of introductory material, it covers topics including protein structure prediction and simulation. Publisher Description This book is an edited volume, the goal of which is to pr
Medical Book Bayesian Methods in Structural Bioinformatics
It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.
