Complex Stochastic Systems

O.E. Barndorff-Nielsen, Claudia Kluppelberg, D. R. Cox

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English

Chapman & Hall/CRC
09 August 2000

Probability & statistics; Stochastics

Complex stochastic systems comprises a vast area of research, from modelling specific applications to model

fitting, estimation procedures, and computing issues. The exponential growth in computing power over the last two decades has revolutionized statistical analysis and led to rapid developments and great progress in this emerging field. In Complex Stochastic Systems, leading researchers address various statistical aspects of the field, illustrated by some very concrete applications.

A Primer on Markov Chain Monte Carlo

by Peter J. Green provides a wide-ranging mixture of the mathematical and statistical ideas, enriched with concrete examples and more than 100 references.

Causal Inference from Graphical Models by Steffen L. Lauritzen explores causal concepts in connection with modelling complex stochastic systems, with focus on the effect of interventions in a given system.

State Space and Hidden Markov Models by Hans R. Knnschshows the variety of applications of this concept to time series in engineering, biology, finance, and geophysics.

M onte Carlo Methods on Genetic Structures by Elizabeth A. Thompson investigates special complex systems and gives a concise introduction to the relevant biological methodology.

Renormalization of Interacting Diffusions by Frank den Hollander presents recent results on the large space-time

behavior of infinite systems of interacting diffusions.

Stein's Method for Epidemic Processes by Gesine Reinert investigates the mean field behavior of a general stochastic epidemic with explicit bounds.

Individually, these articles provide authoritative, tutorial-style exposition and recent results from various subjects related to complex stochastic systems.

Collectively, they link these separate areas of study to form the first comprehensive overview of this rapidly developing field.

By: D. R. Cox
Edited by: O.E. Barndorff-Nielsen, Claudia Kluppelberg
Imprint: Chapman & Hall/CRC
Country of Publication: United States
Volume: 87
Dimensions: Height: 229mm, Width: 152mm, Spine: 22mm
Weight: 720g
ISBN: 9781584881582
ISBN 10: 1584881585
Pages: 306
Publication Date: 09 August 2000
Audience: College/higher education , Professional and scholarly , Professional & Vocational , A / AS level , Further / Higher Education
Format: Hardback
Publisher's Status: Active

A Primer on Markov Chain Monte Carlo. Causal Inference from Graphical Models. State Space and Hidden Markov Models. Monte Carlo Methods on Genetic Structures. Renormalization of Interacting Diffusions. Stein's Method for Epidemic Processes.

Ole E. Barndorff-Nielsen, David R. Cox, Claudia Klüppelberg

Reviews for Complex Stochastic Systems

"""this book has achieved its aim of providing well-written tutorial papers for researchers by leading experts in several important areas of statisticsthe book as a whole is well deserving of a position on any researcher statistician's bookshelf"" --N. Sheehan, Biometrics, June 2001 ""[includes] an outstanding primer on Markov chain Monte Carlo (MCMC)it is one of the best available tutorial sources on contemporary MCMC procedures."" --Journal of Mathematical Psychology ""One often has reservations about edited volumes, but this one is an excellent introduction to some of the most important tools of modern statistics."" -Short Book Reviews, Vol. 21, No. 2, August 2001"