This paper provides a construction of a Fleming-Viot measure valued diffusion process, for which the transition function is known, by extending recent ideas of Gibbs sampler based Markov processes. In particular, we concentrate on the Chapman-Kolmogorov consistency conditions which allows a...

This paper develops a new family of Bayesian semiparametric models. A particular member of this family is used to model option prices with the aim of improving out-of-sample predictions. A detailed empirical analysis is made for European index call and put options to illustrate the ideas

We provide details on the full reconstruction of the dynamic equations from measured time series data, given the general class of the underlying physical process. Our results can be used by researchers in physical modelling and statistical mechanics interested in an efficient estimation of low...

In this paper, it is demonstrated that an extension to the exponential power family allows for robustness characteristics for the normal location parameter problem, previously thought to be restricted to the Student-t and a subclass of the positive stable families.

We show that the cumulative distribution function corresponding to a kernel density estimator with optimal bandwidth lies outside any confidence interval, around the empirical distribution function, with probability tending to 1 as the sample size increases.

Let Ω be a space of densities with respect to some "σ"-finite measure "μ" and let <b>Π</b> be a prior distribution having support Ω with respect to some suitable topology. Conditional on "f", let <b>X</b>-super-<b>n</b> = ("X"₁&hairsp;,…, &hairsp;"X"_"n") be an independent and identically distributed sample of size <b>"n"</b>...