We provide a framework which admits a number of ``marginal'' sequential Monte Carlo (SMC) algorithms as particular cases -- including the marginal particle filter [Klaas et al., 2005, in: Proceedings of Uncertainty in Artificial Intelligence, pp. 308--315], , the independent particle filter [Lin et al., 2005, Journal of the American Statistical Association 100, pp. 1412--1421] and linear-cost Approximate Bayesian Computation SMC [Sisson et al., 2007, Proceedings of the National Academy of Sciences (USA) 104, pp. 1760--1765.]. We provide conditions under which such algorithms obey laws of large numbers and central limit theorems and provide some further asymptotic characterizations. Finally, it is shown that the asymptotic variance of a class of estimators associated with certain marginal SMC algorithms is never greater than that of the estimators provided by a standard SMC algorithm using the same proposal distributions.

翻译：我们提供了一个框架,将一些“边际”连续的Monte Carlo(SMC)算法作为特例 -- -- 包括边际粒子过滤器[Klaas等人,2005年,载于:《人工智能中不确定性的处理》,第308-315页),独立粒子过滤器[Lin等人,2005年,《美国统计协会杂志》100,第1412-1421页]和线性成本的“近边巴伊西亚算法”SMC[Sisson等人,2007年,《国家科学院记录》(USA)104,第1760-1765页]。我们提供了这些算法遵守大量法律及中央限值理论的条件,并提供了一些进一步的零用特征描述。最后,我们表明,与某些边际SMC算法相关的一个类估算师的无谓差异从未超过使用相同建议分布标准SMC算法提供的估算师的无谓差异。</s>