标题： 基于广义双滤波平滑法的隐马尔可夫模型的边缘似然计算 显示英文标题

标题： Marginal Likelihood Computation for Hidden Markov Models via Generalized Two-Filter Smoothing

摘要： 本文中我们引入了一种针对隐马尔可夫模型（HMM）的边缘似然估计，该估计利用广义两滤波平滑分解的序列蒙特卡罗（SMC）近似方法（Briers, 2010）。 证明了此估计是无偏的，并建立了中心极限定理（CLT）。 后者的CLT还允许证明关于联合平滑分布的边缘期望估计的CLT；这些构成了与广义两滤波平滑分解的SMC近似相关的首批理论结果之一。 从数值角度探讨了新估计及其应用。 显示英文摘要

摘要： In this note we introduce an estimate for the marginal likelihood associated to hidden Markov models (HMMs) using sequential Monte Carlo (SMC) approximations of the generalized two-filter smoothing decomposition (Briers, 2010). This estimate is shown to be unbiased and a central limit theorem (CLT) is established. This latter CLT also allows one to prove a CLT associated to estimates of expectations w.r.t. a marginal of the joint smoothing distribution; these form some of the first theoretical results associated to the SMC approximation of the generalized two-filter smoothing decomposition. The new estimate and its application is investigated from a numerical perspective.