Addressing Computational Challenges in Bayesian Perception

 

Bobby Ikonomov

The Bayesian model of perception assumes that the brain uses prior information based on past experiences in order to process sensory stimuli in a fashion similar to Bayesian inference. However, existing Bayesian inference methods often involve computational challenges that limit their applicability. This project aims to improve these existing inference methods, develop new ones, and use them to further our understanding of perception. Ultimately, this could lead to predictive algorithms which are faster and more accurate than traditional ones.

 

Supervisors: Michael Gutmann & Peggy Series/Chris Williams