Complex Stochastic Systems

Complex Stochastic Systems

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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. Kunschshows the variety of applications of this concept to time series in engineering, biology, finance, and geophysics. Monte 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 more

Product details

  • Hardback | 304 pages
  • 162.6 x 237.2 x 22.4mm | 592.2g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 2002.
  • 1584881585
  • 9781584881582

Review quote

"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 2001show more

Table of contents

A PRIMER ON MARKOV CHAIN MONTE CARLO, Peter J. Green Introduction Getting Started: Bayesian Inference and the Gibbs Sampler MCMC-The General Idea and the Main Limit Theorems Recipes for Constructing MCMC Methods The Role of Graphical Models Performance of MCMC Methods Reversible Jump Methods Some Tools for Improving Performance Coupling from the Past (CFTP) Miscellaneous Topics Some Notes on Programming MCMC Conclusions CAUSAL INFERENCE FROM GRAPHICAL MODELS, Steffen L. Lauritzen Introduction Graph Terminology Conditional Independence Markov Properties for Undirected Graphs The Directed Markov Property Causal Markov Models Assessment of Treatment Effects in Sequential Trials Identifiability of Causal Effects Structural Equation Models Potential Responses and Counterfactuals Other Issues STATE SPACE AND HIDDEN MARKOV MODELS, Hans R. Kunsch Introduction The General State Space Model Filtering and Smoothing Recursions Exact and Approximate Filtering and Smoothing Monte Carlo Filtering and Smoothing Parameter Estimation Extensions of the Model MONTE CARLO METHODS ON GENETIC STRUCTURES, Elizabeth A. Thompson Genetics, Pedigrees, and Structured Systems Computations on Pedigrees MCMC Methods for Multilocus Genetic Data Conclusion RENORMALIZATION OF INTERACTING DIFFUSIONS, Frank den Hollander Introduction The Model Interpretation of the Model Block Averages and Renormalization The Hierarchical Lattice The Renormalization Transformation Analysis of the Orbit Higher-Dimensional State Spaces Open Problems Conclusion STEIN'S METHOD FOR EPIDEMIC PROCESSES, Gesine Reinert Introduction A Brief Introduction to Stein's Method The Distance of the GSE to its Mean Field Limit Discussionshow more