9:00-9:15
Jun Liu
Opening Remark
9:15-10:00
Wing Wong
Exploiting The Temperature-Energy Scale for Monte Carlo Sampling: Review and Speculations
10:30-11:15
Mike West, Adrian Dobra
Challenges in Stochastic Computation, and Sampling Posterior Distributions over Spaces of Contingency Tables
11:15-12:00
Xiao-Li Meng
Inference with Monte Carlo Data: The Problem of ``Knowing Too Much"
Lunch
1:30-2:10
(A) Ming-Hui Chen
Dimesion-Reduction Methods for Computing Marginal Likelihoods from the MCMC Output
(B) Susan Holmes
Stein's Method Applied to MCMC
2:10-2:50
(A) Ying Nian Wu
MCMC for Overcomplete ICA
(B) Steve Brooks
Recent Innovations for Trans-Dimensional Markov Chains
2:50-3:30
(A) Song Chun Zhu
Image Parsing by Data Driven Markov Chain Monte Carlo
(B) Xiaodong Wang
3:50-4:30
(A) Brad Carlin
Hierarchical Modeling of Covariate-Adjusted Spatial CDFs, with Application to Air Pollutant Data
(B) Mark Huber
Introduction to the Randomness Recycler
4:30-5:10
(A) Edward George
DILUTION PRIORS FOR MODEL UNCERTAINTY
(B) Art Owen
Quasi-Monte Carlo
5:10-5:50
(A) Simon Godsill
Annealed, layered and auxiliary particle filters
(B) Vincent Poor
Statistical Problems in Wireless Communications
6:30-10:00
9:00-9:45
Persi Diaconis
Hit and Run as A Unifying Idea for Designing MCMC algorithms
9:45-10:30
Chip Lawrence
Recursive MCMC Inferences of the Synergistic Binding of Multiple Regulatory Transcription Factors
10:50-11:30
(A) Andrew Gelman
Parameterization and modeling
(B) Antonietta Mira
Performance of time-invariance estimators
11;30-12:10
(A) Radford Neal
Two Practical Uses of Coupling: Circular Coupling and Coupling to an Approximating Chain
(B) David van Dyk
INTRODUCING INCOMPATIBILITY INTO GIBBS SAMPLERS TO IMPROVE CONVERGENCE
(A) Michael Newton
MCMC sampling for a cancer network model
(B) Chuanhai Liu
PX-EM and Beyond
(A) Christophe Andrieu
Controlled MCMC for Automatic Sampler Calibration
(B) Jie Liang
Statistical Geometry of Packing Defects of Lattice Chain Polymer from Sequential Monte Carlo Method
(A) Yazhen Wang
Variance Reduction in Monte Carlo Integration
(B) Raoul Lepage
WHAT HAS RECOVERING A DENSITY FROM SOME OF ITS INTEGRALS TO DO WITH PRICING AN OPTION?
(A) David Madigan
Bayesian analysis of massive datasets via particle filters
(B) Sam Kou
Statistical Analysis of Single Molecule Experiments in Chemistry
(A) Vikram Krishnamurthy
Tracking Results in Discrete Stochastic Approximation
(B) Arnaud Doucet
Optimization of Particle Methods using Stochastic Approximation
(A) Chiara Sabatti
Multiresolution MCMC and Gene Expression Arrays
(B) Yuguo Chen
Adaptive Filtering and Smoothin g in Hidden Markov Models via Sequential Monte Carlo Methods