This article serves as an introduction and survey for economists to the field of sequential
Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo …

Computing smoothing distributions, the distributions of one or more states conditional on
past, present, and future observations is a recurring problem when operating on general …

This paper concerns the use of sequential Monte Carlo methods (SMC) for smoothing in
general state space models. A well-known problem when applying the standard SMC …

Under multiplicative drift and other regularity conditions, it is established that the asymptotic
variance associated with a particle filter approximation of the prediction filter is bounded …

Abstract Let (Y_k)_k∈Z be a stationary sequence on a probability space (Ω,A,P) taking
values in a standard Borel space Y. Consider the associated maximum likelihood estimator …

This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter on the space of sets of
tree trajectories for multiple target tracking with spawning targets. A tree trajectory contains …

This paper discusses variance estimation in sequential Monte Carlo methods, alternatively
termed particle filters. The variance estimator that we propose is a natural modification of …

This paper discusses particle filtering in general hidden Markov models (HMMs) and
presents novel theoretical results on the long-term stability of bootstrap-type particle filters …

Online Expectation Maximization based algorithms for inference in Hidden Markov Models
Page 1 Electronic Journal of Statistics Vol. 7 (2013) 763–792 ISSN: 1935-7524 DOI …

We prove that bootstrap-type Monte Carlo particle filters approximate the optimal nonlinear
filter in a time average sense uniformly with respect to the time horizon when the signal is …