Recursive Monte Carlo algorithms for parameter estimation in general state space models

信号处理 Pub Date : 2001-08-06 DOI:10.1109/SSP.2001.955210

C. Andrieu, A. Doucet

引用次数: 8

Abstract

We present new algorithms that aim at estimating the "static" parameters of a latent variable process in an on-line manner. This new class of on-line algorithms is inspired by Monte Carlo Markov chain (MCMC) methods whose use has been mainly restricted to static problems, i.e., for which the set of observations is fixed. The main interest of this new class of algorithms is that it combines MCMC and particle filtering techniques, for which extensive know-how and literature are now available.

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一般状态空间模型参数估计的递归蒙特卡罗算法

我们提出了新的算法，旨在以在线方式估计潜在变量过程的“静态”参数。这一类新的在线算法受到蒙特卡罗马尔可夫链(MCMC)方法的启发，这些方法的使用主要局限于静态问题，即观测集是固定的。这类新算法的主要兴趣在于它结合了MCMC和粒子过滤技术，目前已有大量的专门知识和文献可供使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

信号处理

自引率

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5812

期刊介绍： Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.