A0755
Title: Variance change point detection with credible sets
Authors: Oscar Hernan Madrid Padilla - UCLA (United States) [presenting]
Lorenzo Cappello - Universitat Pompeu Fabra (Spain)
Abstract: A novel Bayesian approach is introduced to detect changes in the variance of a Gaussian sequence model, focusing on quantifying the uncertainty in the change point locations and providing a scalable algorithm for inference. Such a measure of uncertainty is necessary when change point methods are deployed in sensitive applications, for example, when one is interested in determining whether an organ is viable for transplant. The focus is on framing the problem as a product of multiple single changes in the scale parameter. The model is fit through an iterative procedure similar to what is done for additive models. The novelty is that each iteration returns a probability distribution on time instances, which captures the uncertainty in the change point location. Leveraging a recent result in the literature, the proposal is shown as a variational approximation of the exact model posterior distribution. The algorithm's convergence and the change point localization rate are studied. Extensive experiments in simulation studies and real data illustrate the usefulness of the proposed approach.
