ITERATED FILTERING
ITERATED FILTERING
复制标题
DOI：
10.1214/11-aos886
复制
发表时间：
2011-06-01
影响因子：
4.5
通讯作者：
King, Aaron
中科院分区：
文献类型：
--
作者：
Ionides, Edward L.;Bhadra, Anindya;King, Aaron
在许多科学和工程应用中，部分观测马尔可夫过程模型的推理一直是一个长期存在的方法论挑战。迭代滤波算法通过求解一系列递归的滤波问题来最大化部分观测马尔可夫过程模型的似然函数。我们提出了关于通过顺序蒙特卡罗滤波器实现的迭代滤波算法收敛性的新理论结果。该理论补充了越来越多的经验证据，即迭代滤波算法为非线性动态系统的科学模型提供了有效的推理策略。我们理论的第一步涉及研究一种新的递归方法，用于最大化潜在变量模型的似然函数，当这种似然通过重要抽样进行评估时。这导致考虑迭代重要性采样算法，该算法作为迭代滤波的一个简单的特殊情况，并且可能具有其自身的适用性。
Inference for partially observed Markov process models has been a long-standing methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of filtering problems. We present new theoretical results pertaining to the convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This theory complements the growing body of empirical evidence that iterated filtering algorithms provide an effective inference strategy for scientific models of nonlinear dynamic systems. The first step in our theory involves studying a new recursive approach for maximizing the likelihood function of a latent variable model, when this likelihood is evaluated via importance sampling. This leads to the consideration of an iterated importance sampling algorithm which serves as a simple special case of iterated filtering, and may have applicability in its own right.