Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

State-space models can be used to incorporate subject knowledge on the underlying
dynamics of a time series by the introduction of a latent Markov state process. A user can …

This paper develops and illustrates a new maximum-likelihood based method for the
identification of Hammerstein–Wiener model structures. A central aspect is that a very …

Monte Carlo methods, in particular those based on Markov chains and on interacting particle
systems, are by now tools that are routinely used in machine learning. These methods have …

We derive and study sequential quasi Monte Carlo (SQMC), a class of algorithms obtained
by introducing QMC point sets in particle filtering. SQMC is related to, and may be seen as …

This tutorial aims to provide an accessible introduction to particle filters, and sequential
Monte Carlo (SMC) more generally. These techniques allow for Bayesian inference in …

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space
models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo …

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for
inference in non-linear non-Gaussian state-space models. We propose a new SMC …

In state–space models, smoothing refers to the task of estimating a latent stochastic process
given noisy measurements related to the process. We propose an unbiased estimator of …

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 …