Complex Stochastic Systems
Monographs on statistics and applied probability · 2000
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 Discussion