Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models

作者：Bhadra Anindya^*; Ionides Edward L

来源：Statistics and Computing, 2016, 26(1-2): 393-407.

DOI：10.1007/s11222-014-9513-x

• Approximating meta-models
• Optimization
• Particle filters
• Sequential Monte Carlo
• Variance reduction

Sequential Monte Carlo (SMC) filters (also known as particle filters) are widely used in the analysis of non-linear and non-Gaussian time series models in diverse application areas such as engineering, finance, and epidemiology. When a time series contains an observation that is very unlikely given the previous observations, evaluation of its conditional log likelihood by SMC can suffer from high variance. The presence of one or more such observations can result in poor Monte Carlo estimate of the overall likelihood. In this article, we develop a novel strategy of particle allocation for off-line iterated SMC based filters, in order to reduce the overall variance of the likelihood estimate to enable efficient computation. The complications arising from the intractability of the actual SMC variance is handled via an approximating meta-model, in which we model the SMC errors in the evaluation of conditional log likelihood of the observations as an autoregressive process. We demonstrate numerical results on both simulated and real data sets where adaptive particle allocation results in 54% lower overall variance over the naive equal allocation of particles at all time points in simulations and 53 % lower variance on a real time series model of epidemic malaria transmission. The approximating model approach presented in this article is novel in the context of SMC and offers a computationally attractive procedure for practical analysis of a broad class of time series models.

• 出版日期2016-1