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Bayesian Methods in Structural Bioinformatics (Hardcover, 2012): Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg Bayesian Methods in Structural Bioinformatics (Hardcover, 2012)
Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg
R2,937 Discovery Miles 29 370 Ships in 10 - 15 working days

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.

Bayesian Methods in Structural Bioinformatics (Paperback, 2012 ed.): Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg Bayesian Methods in Structural Bioinformatics (Paperback, 2012 ed.)
Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg
R2,882 Discovery Miles 28 820 Ships in 10 - 15 working days

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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