Authors:
Xiongming Dai
and
Gerald Baumgartner
Affiliation:
Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, 70803, LA, U.S.A.
Keyword(s):
Resampling, Sequential Monte Carlo, Hidden Markov Model, Repetitive Ergodicity, Deterministic Domain.
Abstract:
A resampling scheme provides a way to switch low-weight particles for sequential Monte Carlo with higherweight particles representing the objective distribution. The less the variance of the weight distribution is, the
more concentrated the effective particles are, and the quicker and more accurate it is to approximate the hidden
Markov model, especially for the nonlinear case. Normally the distribution of these particles is skewed, we
propose repetitive ergodicity in the deterministic domain with the median for resampling and have achieved the
lowest variances compared to the other resampling methods. As the size of the deterministic domain M ≪ N
(the size of population), given a feasible size of particles under mild assumptions, our algorithm is faster than
the state of the art, which is verified by theoretical deduction and experiments of a hidden Markov model in
both the linear and non-linear cases.