A0729
Title: Posterior sampling from truncated Ferguson-Klass representation of normalized completely random measure mixtures
Authors: Junyi Zhang - The Hong Kong Polytechnic University (Hong Kong) [presenting]
Angelos Dassios - London Scool of Economics (United Kingdom)
Abstract: The finite approximation of the completely random measure (CRM) is introduced by truncating its Ferguson-Klass representation. The approximation is obtained by keeping the N largest atom weights of the CRM unchanged and combining the smaller atom weights into a single term. The simulation algorithms are developed for the approximation and characterize its posterior distribution, for which a blocked Gibbs sampler is devised. The approximation is used in two models. The first assumes such an approximation as the mixing distribution of a Bayesian nonparametric mixture model and leads to a finite approximation to the model posterior. The second concerns the finite approximation to the Caron-Fox model. Examples and numerical implementations are given based on the gamma, stable and generalized gamma processes.
