Adaptive importance sampling in signal processing
Adaptive importance sampling in signal processing
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DOI：
10.1016/j.dsp.2015.05.014
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发表时间：
2015-12-01
影响因子：
2.9
通讯作者：
Corander, Jukka
中科院分区：
文献类型：
--
作者：
Bugallo, Monica F.;Martino, Luca;Corander, Jukka
在贝叶斯信号处理中，关于感兴趣的未知量的所有信息都包含在它们的后验分布中。未知量可以是模型的参数，或者是模型及其参数。在许多重要问题中，这些分布不可能以解析形式获得。另一种方法是通过基于蒙特卡罗的方法生成近似值，如马尔可夫链蒙特卡罗（MCMC）采样，自适应重要性采样（AIS）或粒子滤波（PF）。虽然MCMC抽样和PF在文献中受到了相当大的关注，并且得到了相当好的理解，但AIS方法仍然相对未被探索。本文回顾了AIS的基础知识，并对该主题的最新技术进行了全面的调查。它的一些最相关的实现重新审视和比较，通过计算机模拟的例子。(C)2015 Elsevier Inc. All rights reserved.
In Bayesian signal processing, all the information about the unknowns of interest is contained in their posterior distributions. The unknowns can be parameters of a model, or a model and its parameters. In many important problems, these distributions are impossible to obtain in analytical form. An alternative is to generate their approximations by Monte Carlo-based methods like Markov chain Monte Carlo (MCMC) sampling, adaptive importance sampling (AIS) or particle filtering (PF). While MCMC sampling and PF have received considerable attention in the literature and are reasonably well understood, the AIS methodology remains relatively unexplored. This article reviews the basics of AIS as well as provides a comprehensive survey of the state-of-the-art of the topic. Some of its most relevant implementations are revisited and compared through computer simulation examples. (C) 2015 Elsevier Inc. All rights reserved.