Online Program Home
My Program

Abstract Details

Activity Number: 230 - Recent Advances in Nonexchangeable, Dependent, Random Partition and Feature Allocation Models
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #329048 Presentation
Title: Exploiting Conjugacy to Build Time Dependent Feature Allocation Models
Author(s): Raffaele Argiento* and Ilaria Bianchini and Jim Edward Griffin
Companies: Università di Torino and Politecnico de Milano and University of Kent
Keywords: Bayesian Nonparametrics; Completely Random Measures ; Feature Allocation; Factor Analysis ; Time Series

A flexible approach to build stationary time-dependent processes exploits the concept of conjugacy in a Bayesian framework: the transition law of the process is defined as the predictive distribution of an underlying Bayesian model. If the model is conjugate, the transition kernel can be analytically derived, making the approach particularly appealing. We aim at achieving such a convenient mathematical tractability in the context of completely random measures (CRMs), i.e. when the variables exhibiting a time dependence are CRMs. In order to take advantage of the conjugacy, we consider the wide family of exponential completely random measures. This leads to a simple description of the process which has a autoregressive structure. The proposed process can be straightforwardly employed to extend CRM-based Bayesian nonparametric models such as feature allocation models to time-dependent data. These processes can be applied to problems from modern real life applications in very different fields, from computer science to biology. Here, we develop a dependent latent feature model for the identification of features in images and a dynamic Poisson factor analysis for topic modelling.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program