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NEW: The program schedule outline now includes links to slides for some of the talks.

A google map of the Workshop sites can be found here.

The Monte Carlo method was initially developed for numerical integrations in statistical physics problems during the early days of electronic computing (1945-55). The past two decades have witnessed a strong surge of interest in Monte Carlo methods from the scientific community. Due to the rapid progress in computer technology and the need for handling large datasets and complex systems, researchers ranging from computational biologist to engineers and to statisticians now view Monte Carlo techniques as indispensible tools. Besides using the popular Markov chain Monte Carlo strategies, they have also experimented with various sequential Monte Carlo strategies, resulting in an array of novel and effective inferential and optimization tools.

The First and Second Workshops were very enjoyable experiences and all participants were enthusiastic about having another one. Built upon the success of the previous workshops, this current one is organized based on the same principle: to provide a forum for the presentation of recent developments in the efficient design, theoretical analysis, and novel application of the Monte Carlo method, with an emphasis on their relevance to artificial intelligence, bioinformatics, engineering, and statistics. We hope to bring together probabilists, statisticians, engineers, computational biologists, and, most importantly, interested graduate students, to share the exciting developments, to foster new ideas, and to stimulate the exchange of information between specialists in various areas.

The topics of the Workshop include theoretical analyses of MCMC, methods for estimating normalizing constants (e.g., Bayes factors), particle filters and mixture Kalman filters, sequential Monte Carlo optimizations, stochastic approximation, applications in bioinformatics, target tracking, telecommunications, and financial modeling.


Poster presentation from participants are welcomed. Each accepted poster has a space for fifteen 8.5' by 11' pages to display. Women, minorities, and graduate students are strongly encouraged to participate. Conference fee will be waived for each graduate student with an accepted poster abstract submission.


The talks and poster sessions of the workshop are open to the whole Harvard community.


Organizing Committee Chair:
Jun Liu, Professor of Statistics, Harvard University

Organizing Committee:
Rong Chen, Professor of Information and Decision Sciences, University of Illinois at Chicago
Xiaodong Wang, Associate Professor of Electrical Engineering, Columbia University
S.C. Samuel Kou, John L. Loeb Associate Professor of the Natural Sciences, Department of Statistics, Harvard University

Local Organizing Team:
Paul Edlefsen, Gopi Goswami, Steve Qin, Jinfeng Zhang, Dale Rinkel, and Jun Liu

Questions:
jliu@stat.harvard.edu

A poster is now available. A higher-resolution version may be found here.