Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) properties of probability distributions. We introduce context-specific likelihood weighting (CS-LW), a new sampling methodology, which besides exploiting the classical conditional independence properties, also exploits context-specific independence (CSI) properties. Unlike the standard likelihood weighting, CS-LW is based on partial assignments of random variables and requires fewer samples for convergence due to the sampling variance reduction. Furthermore, the speed of generating samples increases. Our novel notion of contextual assignments theoretically justifies CS-LW. We empirically show that CS-LW is competitive with state-of-the-art algorithms for approximate inference in the presence of a significant amount of CSIs.
翻译:在精确推断不切实际时,抽样是一种常见的近似推断方法,一般而言,抽样算法并不利用概率分布的因地制宜性(CSI)特性。我们采用了一种新的抽样方法,即根据具体情况进行加权(CS-LW),这种方法除了利用传统的有条件独立性质外,还利用了因地制宜性(CSI)特性。与标准概率加权不同,CS-LW所依据的是随机变量的局部分配,由于抽样差异的减少,需要较少的样本才能汇合。此外,生成样本的速度加快。我们在理论上认为,从理论上讲,根据具体情况分配的新概念可以证明CS-LW是CS-LW。我们从经验上看,CS-LW在有大量CSI的情况下,与最先进的近似误判算算法具有竞争力。