Sequential Monte Carlo: Past and Present
by Prof. Jun Liu
Abstract: This talk will review historical developments in polymer simulation,
which isclosely related to the recently-popular Monte Carlo particle
filtering, or more precisely, sequential Monte Carlo (SMC) methods.
Two key elements in SMC are sequential importance sampling (SIS) and
resampling (or pruning-enrichment). SIS is a generic, but useful
strategy for building up the trial distribution for a high-dimensional
problem and can be applied naturally to accommodate dynamic systems
and to mimic a learning mechanism. Because of SIS's sequential
structure, one can monitor its importance sampling weights along with
the sequential sampling and make appropriate interference, such as
resampling and rejection sampling, to control Monte Carlo variations.
SIS together with many interference techniques gives rise to a
collection of related methods with the name "sequential Monte Carlo."
We show some success stories of the method in energy minimization for
protein folding, econometric modeling, target tracking, and digital
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