Variational inference is a flexible approach to solving problems of intractability in Bayesian models. Unfortunately the convergence of variational methods is often slow. We review a recently suggested variational approach for approximate inference in Gaussian process (GP) models and show how convergence may be dramatically improved through the use of a positive correction term to the standard variational bound. We refer to the modified bound as a KL-corrected bound. The KL-corrected bound is a lower bound on the true likelihood, but an upper bound on the original variational bound. Timing comparisons between optimisation of the two bounds show that optimisation of the new bound consistently improves the speed of convergence. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
King, N. J., & Lawrence, N. D. (2006). Fast variational inference for Gaussian process models through KL-correction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4212 LNAI, pp. 270–281). Springer Verlag. https://doi.org/10.1007/11871842_28
Mendeley helps you to discover research relevant for your work.