A comparison of approximate Bayesian forecasting methods for non-Gaussian time series

We present the results on the comparison of efficiency of approximate Bayesian methods for the analysis and forecasting of non-Gaussian dynamic processes. A numerical algorithm based on Markov-Chain, Monte-Carlo (MCMC) methods has been developed to carry out the Bayesian analysis of non-linear time series. Although the MCMC-based approach is not fast, it allows us to study the efficiency, in predicting future observations, of approximate propagation procedures that, being algebraic, have the practical advantage of being very quick.