Annual Report 2008.pdf - SAMSI

2. Sequential Monte Carlo Methods
2.1 Introduction
This 12 month SAMSI program will develop new approaches to scientific/statistical
computing using innovative Sequential Monte Carlo (SMC) methods. The program will address
fundamental challenges in developing effective sequential and adaptive simulation methods for
computations underlying inference and decision analysis. The research will blend conceptual
innovation in new and emerging methods with evaluation in substantial applied contexts drawn
from areas such as control, communications and robotics engineering, financial and macroeconomics,
among others. Researchers from statistics, computer science, information
engineering and applied mathematics will be involved, and the program will promote the
opportunity for both methodological and theoretical research. The interdisciplinary aspects of the
program are substantial, as is the attractiveness for students and postdocs.
2.2 Organizing Committee:
Co-Chairs: Arnaud Doucet (British Columbia, Statistics and Computer Science) and Simon
Godsill (Cambridge UK, Information Engineering).
Committee: Monica Bugallo (Stony Brook, Electrical and Computer Engineering), Petar Djuric
(Stony Brook, Electrical and Computer Engineering), Michael Jordan (Berkeley, Statistics and
Computer Science), Jun Liu (Harvard, Statistics), Gareth Roberts (Warwick, Statistics), Raquel
Prado (Santa Cruz, Applied Mathematics and Statistics), Neil Shephard, (Oxford, Statistics and
Econometrics), and Simon Tavare (Cambridge, Computational Biology).
Local Scientific Coordinator: Mike West (Duke, Statistical Science);
National Advisory Council Liaison: Richard Durrett (Cornell University);
Directorate Liaisons: James Berger and Michael Minion
2.3 Background
Monte Carlo (MC) methods are central to modern numerical modelling and computation
in complex systems. Markov chain Monte Carlo (MCMC) methods provide enormous scope for
realistic statistical modelling and have attracted much attention from disciplinary scientists as
well as research statisticians. Many scientific problems are not, however, naturally posed in a
form accessible to evaluation via MCMC, and many are inaccessible to such methods in any
practical sense. For example, for real-time, fast data processing problems that inherently involve
sequential analysis, MCMC methods are often not obviously appropriate at all due to their
inherent "batch" nature. The recent emergence of sequential MC concepts and techniques has led
to a swift uptake of basic forms of sequential methods across several areas, including
communications engineering and signal processing, robotics, computer vision and financial time
series. This adoption by practitioners reflects the need for new methods and the early successes
and attractiveness of SMC methods. In such, probability distributions of interest are
approximated by large clouds of random samples that evolve as data is processed using a
combination of sequential importance sampling and resampling ideas. Variants of particle
filtering, sequential importance sampling, sequential and adaptive Metropolis MC and stochastic
search, and others have emerged and are becoming popular for solving variants of "filtering"
problems; i.e. sequentially revising sequences of probability distributions for complex statespace
models. Useful entree material and examples SMC methods can be found at the SMC
preprint site.