Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 181, No. 3 (2018), pp. 635-647 (13 pages) Statistical agencies are increasingly adopting synthetic data methods for ...

Bayesian nonparametric mixture models represent a powerful statistical framework that extends traditional mixture modelling by allowing the number of mixture components to be inferred from the data ...

We propose a nested Gaussian process (nGP) as a locally adaptive prior for Bayesian nonparametric regression. Specified through a set of stochastic differential equations (SDEs), the nGP imposes a ...

The covariance matrix of asset returns is the key input for many problems in finance and economics. This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from ...

Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...