Date/Time
Date(s) - 29/10/2024
3:30 pm - 4:30 pm

Location: MDCL 3020

Speaker: Camila Pedroso Estevam de Souza (Western University)

Title: Advancing Functional Data Clustering and Survival Analysis with Variational Inference

Abstract: Variational Inference (VI) is a method for analytically approximating the posterior distribution in Bayesian models, offering a more computationally efficient alternative to Markov Chain Monte Carlo (MCMC) sampling techniques. In this talk, I will present work from two recent publications, co-authored with my students and collaborators. The first paper applies VI to functional data clustering, where the goal is to identify groups of curves without prior group membership information. Using a B-spline regression mixture model with random intercepts, we developed a novel variational Bayes (VB) algorithm for simultaneous clustering and smoothing of functional data. The second paper focuses on survival data analysis, proposing a VB algorithm for inferring the parameters of the log-logistic accelerated failure time model by incorporating a piecewise approximation technique to address intractable calculations and achieve Bayesian conjugacy. In both papers, we conducted extensive simulation studies to assess the performance of the proposed VB algorithms, comparing them with other methods, including MCMC algorithms. Applications to real data illustrate the methodologies’ practical use. The proposed VB algorithms demonstrate excellent performance in clustering functional data and analyzing survival data while significantly reducing computational costs compared to MCMC methods. The links to the papers are as follows: https://doi.org/10.1007/s11634-024-00590-w and https://doi.org/10.1007/s11222-023-10365-6.