Particle-Based Adaptive-Lag Online Marginal Smoothing in General State-Space Models

被引：4

作者：

Alenlov, Johan ^{[1
]}

Olsson, Jimmy ^{[2
]}

机构：

[1] Uppsala Univ, Dept Informat Technol, S-75236 Uppsala, Sweden

[2] KTH Royal Inst Technol, Dept Math, S-11428 Stockholm, Sweden

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2019年 / 67卷 / 21期

基金：

瑞典研究理事会;

关键词：

Smoothing methods; Approximation algorithms; Markov processes; Signal processing algorithms; Monte Carlo methods; Hidden Markov models; Biological system modeling; Sequential Monte Carlo methods; state-space models; marginal smoothing; PaRIS; particle filters; state estimation; HIDDEN MARKOV-MODELS; MONTE-CARLO METHODS; ALGORITHM; FILTER;

D O I：

10.1109/TSP.2019.2941066

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We present a novel algorithm, an adaptive-lag smoother, approximating efficiently, in an online fashion, sequences of expectations under the marginal smoothing distributions in general state-space models. The algorithm evolves recursively a bank of estimators, one for each marginal, in resemblance with the so-called particle-based, rapid incremental smoother (PaRIS). Each estimator is propagated until a stopping criterion, measuring the fluctuations of the estimates, is met. The presented algorithm is furnished with theoretical results describing its asymptotic limit and memory usage.

引用

页码：5571 / 5582

页数：12

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