Non-Gaussian state space modeling of time series

A non Gaussian state space approach to the analysis of time series is shown. The model is expressed in general state space form which is expressed by a conditional distributions. General non-Gaussian filtering and smoothing formulae are shown and two numerical approximations to related distributions are used to realize these formulae. Significant merit of non Gaussian modeling is illustrated by some numerical examples.