Heteroscedastic Relevance Vector Machine
This is an incremental improvement for Bayesian regression practitioners, as it extends an existing method with heteroscedasticity but lacks finalized results.
The authors proposed a heteroscedastic generalization to the Relevance Vector Machine (RVM), a fast Bayesian regression framework, using variational approximation and expectation propagation methods, but noted that the work is still in progress with results under examination and comparison to previous works.
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.