| 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 |
図書
