sequential Monte Carlo

Bagged filters for partially observed interacting systems

Statistical inference for high-dimensional partially observed, nonlinear, stochastic processes is a methodological challenge with applications including spatiotemporal analysis of epidemiological and ecological systems. Standard particle filter …

Partially observed Markov processes with spatial structure via the R package spatPomp

We address inference for a partially observed nonlinear non-Gaussian latent stochastic system comprised of interacting units. Each unit has a state, which may be discrete or continuous, scalar or vector valued. In biological applications, the state …