标题： 图模型的序列蒙特卡洛 显示英文标题

标题： Sequential Monte Carlo for Graphical Models

摘要： 我们提出了一种使用顺序蒙特卡洛（SMC）算法进行概率图模型（PGM）推理的新框架。通过分解PGM的序列，我们找到了定义在单调递增概率空间序列上的辅助分布序列。通过使用SMC来针对这些辅助分布，我们可以近似由PGM定义的完整联合分布。SMC采样器的一个关键优点是它提供了模型划分函数的一个无偏估计。我们还展示了如何将其用于粒子马尔可夫链蒙特卡洛框架内，以构建通用PGM的高维块采样算法。 显示英文摘要

摘要： We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs.