bayesian methods structural bioinformatics (8 Ergebnisse)

Hardcover. Zustand: Brand New. 1st edition. 385 pages. 9.25x6.00x0.75 inches. In Stock.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 408 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Paperback. Zustand: Brand New. 2012 edition. 408 pages. 9.25x6.10x0.90 inches. In Stock.

Zustand: 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. Editor(s): Hamelryck, Thomas; Mardia, Kanti V.; Ferkinghoff-Borg, Jesper. Series: Statistics for Biology and Health. Num Pages: 408 pages, 30 black & white tables, biography. BIC Classification: MBGR; PBT; PDE; PHVN; PSA. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 21. Weight in Grams: 623. . 2014. Paperback. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 408 pp. Englisch.

Zustand: 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. Editor(s): Hamelryck, Thomas; Mardia, Kanti V.; Ferkinghoff-Borg, Jesper. Series: Statistics for Biology and Health. Num Pages: 408 pages, 30 black & white tables, biography. BIC Classification: MBF; PBT; PHVN; PSA. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 25. Weight in Grams: 771. . 2012. 2012th Edition. Hardcover. . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.