Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter.

ABSTRACT: We propose a method for inference on moderately high-dimensional, nonlinear, non-Gaussian, partially observed Markov process models for which the transition density is not analytically tractable. Markov processes with intractable transition densities arise in models defined implicitly by simulation algorithms. Widely used particle filter methods are applicable to nonlinear, non-Gaussian models but suffer from the curse of dimensionality. Improved scalability is provided by ensemble Kalman filter methods, but these are inappropriate for highly nonlinear and non-Gaussian models. We propose a particle filter method having improved practical and theoretical scalability with respect to the model dimension. This method is applicable to implicitly defined models having analytically intractable tr

SUBMITTER: Park J 

PROVIDER: S-EPMC9164307 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature