State space methods have proven indispensable in neural data analysis. However, common
methods for performing inference in state-space models with non-Gaussian observations …

A new algorithm for the prediction, filtering, and smoothing of non-Gaussian nonlinear state
space models is shown. The algorithm is based on a Monte Carlo method in which …

Since our first edition of this book, many developments in statistical mod elling based on
generalized linear models have been published, and our primary aim is to bring the book up …

We define a subclass of dynamic linear models with unknown hyperpara‐meter called d‐
inverse‐gamma models. We then approximate the marginal probability density functions of …

Probabilistic inference is the problem of estimating the hidden variables (states or
parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete …

This book contains a up-to-date overview of nonlinear time series models and their
application to modelling economic relationships. It considers nonlinear models in stationary …

We consider the problem of implementing simple and efficient Markov chain Monte Carlo
(MCMC) estimation algorithms for state space models. A conceptually transparent derivation …

The analysis of non-Gaussian time series by using state space models is considered from
both classical and Bayesian perspectives. The treatment in both cases is based on …

Latent variable time-series models are among the most heavily used tools from machine
learning and applied statistics. These models have the advantage of learning latent structure …

Spatio-temporal statistical models are increasingly being used across a wide variety of
scientific disciplines to describe and predict spatially-explicit processes that evolve over …