Sequential Monte Carlo algorithms for a class of outer measures

2017/08/22 by Jérémie Houssineau, Houssineau, Jeremie, Branko Ristić +1 · 1 citation
Computer Science · Economics, Econometrics and Finance · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.1708.06489

openalex publication_date 2017/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Closed-form stochastic filtering equations can be derived in a general setting where probability distributions are replaced by some specific outer measures. In this article, we study how the principles of the sequential Monte Carlo method can be adapted for the purpose of practical implementation of these equations. In particular, we explore how sampling can be used to provide support points for the approximation of these outer measures. This step enables practical algorithms to be derived in the spirit of particle filters. The performance of the obtained algorithms is demonstrated in simulations and their versatility is illustrated through various examples.

Citations

• Bayesian data assimilation based on a family of outer measures

Cited by

• Robust TMA using the possibility particle filter

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