In this paper we consider a class of conditionally Gaussian state space models and discuss how they can provide a flexible and fairly simple tool for modelling financial time series, even in presence of different components in the series, or of stochastic volatility. Estimation can be computed...

We propose a general procedure for constructing nonparametric priors for Bayesian inference. Under very general assumptions,the proposed prior selects absolutely continuous distribution functions, hence it can be useful with continuous data. We use the notion of Feller-type approximation, with a...

In this note we raise some questions for Bayesian nonparametric statistics starting from an example. The problems is described by Coram an Diaconis (2001) and regards studying, by probabilistic techniques, the correspondence between the eigenvalues of random unitary matrices and the complex...