May 13 Sunday
8:00-8:45	Breakfast, onsite registration (Harvard Hall 104)
8:45-9:00	Jun Liu Opening Remark
Plenary Session (Harvard Hall 104)
9:00-9:45	Don Rubin Applying MCMC to a Classic Problem in Statistics: Estimating dose-response when only treatment versus placebo is randomized -- the Efron-Feldman (1991 JASA) data reanalyzed
9:45-10:30	Nicholas Polson Optimal Filtering of Jump-Diffusions: Extracting Latent States from Asset Prices
Break
Parallel Sessions
	Session A (Harvard Hall 103)	Session B (Harvard Hall 102)
10:50-11:30	Jie Liang Transition state ensemble of protein folding and evolutionary selection pressure through Sequential Monte Carlo and MCMC	Jim Fill A (Minor) Miracle: Diagonalization of a Bose-Einstein Markov Chain
11:30-12:10	Ulrich H.E. Hansmann In Silico Folding of Small Proteins	Christian Robert A Bayesian reassessment of k-nearest-neighbour classification
Lunch (Harvard Hall 104)
1:30-2:10	Hongyu Zhao Bayesian Methods to Infer Protein Interactions	Xiao-Li Meng Statistical Physics and Statistical Computing: a Critical Link
2:10-2:50	Shane T. Jensen Bayesian Clustering with the Dirichlet Process	David van Dyk Implementing Gibbs-Type Samplers Using Incompatible Draws With Applications in High-Energy Astrophysics
2:50-3:30	Yves Atchade The Wang-Landau algorithm: Applications and some convergence results	Xiaodong Wang Joint Multiple Target Tracking and Classification in Collaborative Sensor Networks
Coffee Break
3:50-4:30	Nancy Zhang Bayesian Variable Selection in Structured High Dimensional Covariate Spaces	Murali Haran Experiments with Monte Carlo methods for spatial models
4:30-5:10	Ming-Hui Chen Objective Bayesian Variable Selection for Logistic Regression Models with Jeffreys's Prior	Arnaud Doucet Particle Markov Chain Monte Carlo
6:00-7:00	H'ours Oeuvres (Sheraton Commander Hotel)
6:00-9:00	Banquet (Sheraton Commander Hotel)
6:00-11:00	Poster session (Sheraton Commander Hotel)

May 14 Monday
8:00-9:00	Breakfast (Harvard Hall 104)
Plenary Session
9:00-9:45	David Landau A new approach to Monte Carlo sampling in statistical physics and beyond
9:45-10:30	Andrew Gelman Weakly informative priors
10:30-11:15	Andrew Barron Adaptive Annealing

Lunch (Harvard Hall 104)

Parallel Sessions
	Sesssion A (Sever Hall 103)	Session B (Sever Hall 203)
1:00-1:40	Simon Godsill Sequential Monte Carlo methods for tracking of partially observed jump processes	Christophe Andrieu Exact Approximate Algorithms for Monte Carlo computations: the Expected Auxiliary Variable (EAV) principle
1:40-2:20	Radford M. Neal Short-cut MCMC: An Alternative to Adaptation	Patrick J. Wolfe Multi-Scale MCMC Methods for Sampling from Products of Gaussian Mixtures
2:20-3:00	Antonietta Mira Adaptive Multiple Importance Sampling (AMIS)	Yuguo Chen A New Data Augmentation Scheme
3:00-3:40	Galin Jones Fixed-Width Output Analysis for Markov Chain Monte Carlo	Radu Craiu Randomized Quasi Monte Carlo for Markov chain Monte Carlo
Coffee Break
3:55-4:35	Jose H. Blanchet Lyapunov-type Inequalities in Importance Sampling and Perfect Simulation	Faming Liang Annealing Evolutionary Stochastic Approximation Monte Carlo for Global Optimization
4:35-5:15	Adrian Barbu Hierarchical Image-Motion Segmentation using Swendsen-Wang Cuts	Jinfeng Zhang Novel Monte Carlo Methods For Protein Structure Modeling

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