Dekorationsartikel gehören nicht zum Leistungsumfang.
Bayesian Networks in R
with Applications in Systems Biology
Taschenbuch von Radhakrishnan Nagarajan (u. a.)
Sprache: Englisch

72,10 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 4-7 Werktage

Kategorien:
Beschreibung
Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using theapproaches presented in the book.
Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using theapproaches presented in the book.
Über den Autor

Radhakrishnan Nagarajan, Ph.D.

Dr. Nagarajan is an Associate Professor in the Division of Biomedical Informatics, Department of Biostatistics at the College of Public Health, University of Kentucky, Lexington, USA. His areas of research falls under evidence-based science that demands knowledge discovery from high-dimensional molecular and observational healthcare data sets using a combination of statistical algorithms, machine learning and network science approaches.

Contact: Division of Biomedical Informatics/Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose Street, MDS 230F, Lexington, KY 40536-0082.

Marco Scutari, Ph.D.

Dr. Scutari studied Statistics and Computer Science at the University of Padova, Italy. He earned his Ph.D. in Statistics in Padova under the guidance of Prof. A. Brogini, studying graphical model learning. He is now Research Associate at the Genetics Institute, University College London (UCL). His research focuses on the theoretical properties of Bayesian networks and their applications to biological data, and he is the author and maintainer of the bnlearn R package.

Contact: Genetics Institute, University College London Darwin Building, Room 212 London, WC1E 6BT United Kingdom.

Sophie Lèbre, Ph.D.

Dr. Lèbre is a Lecturer in the Department of Computer Science at the University of Strasbourg, France.
She originally earned her Ph.D. in Applied Mathematics at the University of Evry-val-d'Essone (France) under the guidance of Prof. B. Prum. Her research focuses on graphical modeling and dynamic Bayesian network inference, devoted to recovering genetic interaction networks from post genomic data. She is the author and maintainer of the G1DBN and the ARTIVA R packages for dynamic Bayesian network inference.

Contact: LSIIT, Equipe BFO, Pôle API, Bd Sébastien Brant - BP 10413, F -67412 Illkirch CEDEX, France.

Contact: Division of Biomedical Informatics/Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose Street, MDS 230F, Lexington, KY 40536-0082.

Zusammenfassung

Represents a unique combination of introduction to concepts and examples from open-source R software

Each chapter is accompanied by examples and exercises with solutions for enhanced understanding and experimentation

Useful for students and researchers across many disciplines

Inhaltsverzeichnis
Introduction.- Bayesian Networks in the Absence of Temporal Information.- Bayesian Networds in the Presence of Temporal Information.- Bayesian Network Inference Algorithms.- Parallel Computing for Bayesian Networks.- Solutions.- Index.- References.
Details
Erscheinungsjahr: 2013
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 172
Reihe: Use R!
Inhalt: xiii
157 S.
36 s/w Illustr.
157 p. 36 illus.
ISBN-13: 9781461464457
ISBN-10: 1461464455
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Nagarajan, Radhakrishnan
Lèbre, Sophie
Scutari, Marco
Auflage: 2013
Hersteller: Springer New York
Springer US, New York, N.Y.
Use R!
Maße: 235 x 155 x 10 mm
Von/Mit: Radhakrishnan Nagarajan (u. a.)
Erscheinungsdatum: 27.04.2013
Gewicht: 0,271 kg
preigu-id: 106127284
Über den Autor

Radhakrishnan Nagarajan, Ph.D.

Dr. Nagarajan is an Associate Professor in the Division of Biomedical Informatics, Department of Biostatistics at the College of Public Health, University of Kentucky, Lexington, USA. His areas of research falls under evidence-based science that demands knowledge discovery from high-dimensional molecular and observational healthcare data sets using a combination of statistical algorithms, machine learning and network science approaches.

Contact: Division of Biomedical Informatics/Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose Street, MDS 230F, Lexington, KY 40536-0082.

Marco Scutari, Ph.D.

Dr. Scutari studied Statistics and Computer Science at the University of Padova, Italy. He earned his Ph.D. in Statistics in Padova under the guidance of Prof. A. Brogini, studying graphical model learning. He is now Research Associate at the Genetics Institute, University College London (UCL). His research focuses on the theoretical properties of Bayesian networks and their applications to biological data, and he is the author and maintainer of the bnlearn R package.

Contact: Genetics Institute, University College London Darwin Building, Room 212 London, WC1E 6BT United Kingdom.

Sophie Lèbre, Ph.D.

Dr. Lèbre is a Lecturer in the Department of Computer Science at the University of Strasbourg, France.
She originally earned her Ph.D. in Applied Mathematics at the University of Evry-val-d'Essone (France) under the guidance of Prof. B. Prum. Her research focuses on graphical modeling and dynamic Bayesian network inference, devoted to recovering genetic interaction networks from post genomic data. She is the author and maintainer of the G1DBN and the ARTIVA R packages for dynamic Bayesian network inference.

Contact: LSIIT, Equipe BFO, Pôle API, Bd Sébastien Brant - BP 10413, F -67412 Illkirch CEDEX, France.

Contact: Division of Biomedical Informatics/Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose Street, MDS 230F, Lexington, KY 40536-0082.

Zusammenfassung

Represents a unique combination of introduction to concepts and examples from open-source R software

Each chapter is accompanied by examples and exercises with solutions for enhanced understanding and experimentation

Useful for students and researchers across many disciplines

Inhaltsverzeichnis
Introduction.- Bayesian Networks in the Absence of Temporal Information.- Bayesian Networds in the Presence of Temporal Information.- Bayesian Network Inference Algorithms.- Parallel Computing for Bayesian Networks.- Solutions.- Index.- References.
Details
Erscheinungsjahr: 2013
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 172
Reihe: Use R!
Inhalt: xiii
157 S.
36 s/w Illustr.
157 p. 36 illus.
ISBN-13: 9781461464457
ISBN-10: 1461464455
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Nagarajan, Radhakrishnan
Lèbre, Sophie
Scutari, Marco
Auflage: 2013
Hersteller: Springer New York
Springer US, New York, N.Y.
Use R!
Maße: 235 x 155 x 10 mm
Von/Mit: Radhakrishnan Nagarajan (u. a.)
Erscheinungsdatum: 27.04.2013
Gewicht: 0,271 kg
preigu-id: 106127284
Warnhinweis

Ähnliche Produkte

Ähnliche Produkte