Fully simplified multivariate normal updates in non-conjugate variational message passing

Publication Type:
Journal Article
Citation:
Journal of Machine Learning Research, 2014, 15 pp. 1351 - 1369
Issue Date:
2014-01-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Wand14.pdfPublished Version440.13 kB
Adobe PDF
Fully simplified expressions for Multivariate Normal updates in non-conjugate variational message passing approximate inference schemes are obtained. The simplicity of these expressions means that the updates can be achieved very eficiently. Since the Multivariate Normal family is the most common for approximating the joint posterior density function of a continuous parameter vector, these fully simplified updates are of great practical benefit. © 2014 Matt P. Wand.
Please use this identifier to cite or link to this item: