Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models. In this paper, we propose a mixed inference algorithm that computes closed-form solutions using belief propagation as much as possible, and falls back to sampling-based SMC methods when exact computations fail. This algorithm thus implements automatic Rao-Blackwellization and is even exact for Gaussian tree models.

翻译：状态空间模型上的精确贝叶斯推断通常是难以处理的，而基础的序贯蒙特卡洛方法对于复杂模型无法给出正确的近似。本文提出了一种混合推断算法，该算法尽可能利用置信传播计算闭式解，并在精确计算失效时退化为基于采样的SMC方法。该算法实现了自动Rao-Blackwellization，且对于高斯树模型甚至能保持精确性。