Title: Bayesian multiscale mixture of Gaussian kernels for density estimation
Authors: Marco Stefanucci - University of Padua (Italy) [presenting]
Antonio Canale - University of Padua (Italy)
Abstract: Some results related to a novel Bayesian nonparametric method for multiscale density estimation are discussed. Specifically, we extend a model originally developed for compact sample spaces to deal with data taking values in the whole real line R. By means of an infinitely-deep binary tree of kernels, we are able to construct a multiscale mixture model able to approximate densities with varying degrees of smoothness and local features. Sampling from the posterior distribution is available with a Markov Chain Monte Carlo method.