The Iterated Auxiliary Particle Filter
提出一种离线迭代粒子滤波器，通过构造最优扭曲模型降低边际似然估计的方差，在挑战性设置中显著优于标准粒子滤波，适用于参数估计。
We present an offline, iterated particle filter to facilitate statistical inference in general state space hidden Markov models. Given a model and a sequence of observations, the associated marginal likelihood L is central to likelihood-based inference for unknown statistical parameters. We define a class of “twisted” models: each member is specified by a sequence of positive functions ψ and has an associated ψ-auxiliary particle filter that provides unbiased estimates of L. We identify a sequence ψ∗ that is optimal in the sense that the ψ∗-auxiliary particle filter’s estimate of L has zero variance. In practical applications, ψ∗ is unknown so the ψ∗ -auxiliary particle filter cannot straightforwardly be implemented. We use an iterative scheme to approximate ψ∗ , and demonstrate empirically that the resulting iterated auxiliary particle filter significantly outperforms the bootstrap particle filter in challenging settings. Applications include parameter estimation using a particle Markov chain Monte Carlo algorithm