Heteroscedastic Relevance Vector Machine

Abstract

In this work we propose a heteroscedastic generalization to RVM, a fast Bayesian framework for regression, based on some recent similar works. We use variational approximation and expectation propagation to tackle the problem. The work is still under progress and we are examining the results and comparing with the previous works.

References in corpus (2)

• Expectation Propagation for approximate Bayesian inference
• Fast Variational Inference in the Conjugate Exponential Family