Fast variational inference in the conjugate exponential family

Hensman, James and Rattray, Magnus and Lawrence, Neil D. (2012) Fast variational inference in the conjugate exponential family. In: Advances in Neural Information Processing Systems. UNSPECIFIED, USA, pp. 2888-2896. ISBN 9781627480031

Full text not available from this repository.


We present a general method for deriving collapsed variational inference algorithms for probabilistic models in the conjugate exponential family. Our method unifies many existing approaches to collapsed variational inference. Our collapsed variational inference leads to a new lower bound on the marginal likelihood. We exploit the information geometry of the bound to derive much faster optimization methods based on conjugate gradients for these models. Our approach is very general and is easily applied to any model where the mean field update equations have been derived. Empirically we show significant speed-ups for probabilistic inference using our bound.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
07 Mar 2017 11:16
Last Modified:
22 Mar 2022 02:35