Sequential Monte Carlo samplers: error bounds and insensitivity to initial conditions

Details Sequential Monte Carlo samplers: error bounds and insensitivity to initial conditions Sequential Monte Carlo samplers: error bounds and insensitivity to initial conditions

2011-03-21

arxiv.org/abs/1103.3970v1

Statistics

Computation

36 pages, 1 figure

Scientific paper

This paper addresses finite sample stability properties of sequential Monte Carlo methods for approximating sequences of probability distributions. The results presented herein are applicable in the scenario where the start and end distributions in the sequence are fixed and the number of intermediate steps is a parameter of the algorithm. Under assumptions which hold on non-compact spaces, it is shown that the effect of the initial distribution decays exponentially fast in the number of intermediate steps and the corresponding stochastic error is stable in \mathbb{L}_{p} norm.

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