It is now over a decade since the pioneering contribution of Gordon (1993), which is
commonly regarded as the first instance of modern sequential Monte Carlo (SMC) …

Data assimilation (DA) holds considerable potential for improving hydrologic predictions as
demonstrated in numerous research studies. However, advances in hydrologic DA research …

Now in its second edition, this accessible text presents a unified Bayesian treatment of state-
of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

Summary Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as
the two main tools to sample from high dimensional probability distributions. Although …

We propose a methodology to sample sequentially from a sequence of probability
distributions that are defined on a common space, each distribution being known up to a …

Modelling based on finite mixture distributions is a rapidly develo** area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …

Two decades ago, with the publication, we witnessed the rebirth of particle filtering (PF) as a
methodology for sequential signal processing. Since then, PF has become very popular …

This contribution is devoted to the comparison of various resampling approaches that have
been proposed in the literature on particle filtering. It is first shown using simple arguments …

Changepoints are abrupt variations in the generative parameters of a data sequence. Online
detection of changepoints is useful in modelling and prediction of time series in application …