Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters
Methodology
2021-02-16 v3 Machine Learning
Abstract
This paper introduces the {\it particle swarm filter} (not to be confused with particle swarm optimization): a recursive and embarrassingly parallel algorithm that targets an approximation to the sequence of posterior predictive distributions by averaging expectation approximations from many particle filters. A law of large numbers and a central limit theorem are provided, as well as an numerical study of simulated data from a stochastic volatility model.
Keywords
Cite
@article{arxiv.2006.15396,
title = {Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters},
author = {Taylor R. Brown},
journal= {arXiv preprint arXiv:2006.15396},
year = {2021}
}