STATISTICS COLLOQUIUM
Derek Aguiar, Assistant Professor
Computer Science and Engineering
University of Connecticut
Bayesian nonparametric modelling and scalable inference in large-scale genomics data
Abstract
Bayesian nonparametric models provide a formal mechanism for encoding probabilistic assumptions about the data generation process where the dimension of the latent space is unknown a priori or may grow with additional samples. A common limitation of these models is that posterior inference is computationally intensive, particularly for nonconjugate models or when integrating over combinatorial structures. In this talk, I will introduce two hierarchical Bayesian nonparametric models and inference algorithms that scale to large genomics data. First, I will describe our mixed-membership model for alternatively spliced transcript discovery with explicit sparsity and inference algorithms based on stochastic variational inference. Second, I will present our genetic sequence clustering model based on fragmentation coagulation processes and how we scale nonconjugate model inference using maximization-expectation. Lastly, I will demonstrate the advantages of our Bayesian nonparametric approach when compared to state-of-the-art methods on simulated and experimental data.
DATE: Wednesday, January 29, 2020
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 344
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu