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 |
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. Künsch |
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 |
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 |
Stein's Method For Epidemic Processes / Gesine Reinert |
A Brief Introduction to Stein's Method |
The Distance of the GSE to its Mean Field Limit |
Discussion |
A Primer On Markov Chain Monte Carlo / Peter J. Green |
Introduction |
Getting Started: Bayesian Inference and the Gibbs Sampler |