Stochastic Modelling for Systems Biology, Second Edition

Stochastic Modelling for Systems Biology, Second Edition
Author :
Publisher : CRC Press
Total Pages : 365
Release :
ISBN-13 : 9781439837726
ISBN-10 : 1439837724
Rating : 4/5 (24 Downloads)

Book Synopsis Stochastic Modelling for Systems Biology, Second Edition by : Darren J. Wilkinson

Download or read book Stochastic Modelling for Systems Biology, Second Edition written by Darren J. Wilkinson and published by CRC Press. This book was released on 2011-11-09 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Re-written to reflect this modern perspective, this second edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. Keeping with the spirit of the first edition, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. New in the Second Edition All examples have been updated to Systems Biology Markup Language Level 3 All code relating to simulation, analysis, and inference for stochastic kinetic models has been re-written and re-structured in a more modular way An ancillary website provides links, resources, errata, and up-to-date information on installation and use of the associated R package More background material on the theory of Markov processes and stochastic differential equations, providing more substance for mathematically inclined readers Discussion of some of the more advanced concepts relating to stochastic kinetic models, such as random time change representations, Kolmogorov equations, Fokker-Planck equations and the linear noise approximation Simple modelling of "extrinsic" and "intrinsic" noise An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional mathematical detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.


Stochastic Modelling for Systems Biology, Second Edition Related Books

Stochastic Modelling for Systems Biology, Second Edition
Language: en
Pages: 365
Authors: Darren J. Wilkinson
Categories: Mathematics
Type: BOOK - Published: 2011-11-09 - Publisher: CRC Press

DOWNLOAD EBOOK

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Ba
Stochastic Modelling for Systems Biology, Third Edition
Language: en
Pages: 405
Authors: Darren J. Wilkinson
Categories: Mathematics
Type: BOOK - Published: 2018-12-07 - Publisher: CRC Press

DOWNLOAD EBOOK

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Ba
Stochastic Modelling for Systems Biology
Language: en
Pages: 360
Authors: Darren James Wilkinson
Categories: Biological systems
Type: BOOK - Published: 2012 - Publisher:

DOWNLOAD EBOOK

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of ""likelihood-free"" methods of
Stochastic Modeling
Language: en
Pages: 305
Authors: Nicolas Lanchier
Categories: Mathematics
Type: BOOK - Published: 2017-01-27 - Publisher: Springer

DOWNLOAD EBOOK

Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reade
An Introduction to Stochastic Modeling
Language: en
Pages: 410
Authors: Howard M. Taylor
Categories: Mathematics
Type: BOOK - Published: 2014-05-10 - Publisher: Academic Press

DOWNLOAD EBOOK

An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich d