Bayesian networks are a concise graphical formalism for describing probabilistic models.
We have provided a brief tutorial of methods for learning and inference in dynamic Bayesian …

This chapter reviews the recent developments in Markov chain Monte Carlo simulation
methods. These methods, which are concerned with the simulation of high dimensional …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics-a
very active area of research in which few up-to-date reference works are available. This is …

Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …

This article analyses the recently suggested particle approach to filtering time series. We
suggest that the algorithm is not robust to outliers for two reasons: The design of the …

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

In this self-contained survey/review paper, we systematically investigate the roots of
Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is …

We provide a tutorial on learning and inference in hidden Markov models in the context of
the recent literature on Bayesian networks. This perspective makes it possible to consider …

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time
according to a finite state Markov chain. In this paper, our aim is to recursively compute …