Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters

• URL: http://arxiv.org/abs/2006.15396v3
• Date: Sun, 14 Feb 2021 00:28:58 GMT
• Title: Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters
• Authors: Taylor R. Brown
• Abstract summary: This paper introduces the it particle swarm filter (not to be confused with particle swarm optimization) It 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 a numerical study of simulated data from a volatility model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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