Informatics Report Series



Related Pages

Report (by Number) Index
Report (by Date) Index
Author Index
Institute Index

Title:Bayesian Hierarchical Mixtures of Experts
Authors: Markus Svensen ; Christopher Bishop
Date: 2003
Publication Title:Proceedings of UAI 2003 (Conference on Uncertainty in Artificial Intelligence)
Publisher:Morgan Kaufmann
Publication Type:Conference Paper Publication Status:Published
Page Nos:57-64
The Hierarchical Mixture of Experts (HME) is a well-known tree-structured model for regression and classification, based on soft probabilistic splits of the input space. In its original formulation its parameters are determined by maximum likelihood, which is prone to severe over-fitting, including singularities in the likelihood function. Furthermore the maximum likelihood framework offers no natural metric for optimizing the complexity and structure of the tree. Previous attempts to provide a Bayesian treatment of the HME model have relied either on local Gaussian representations based on the Laplace approximation, or have modified the model so that it represents the joint distribution of both input and output variables, which can be wasteful of resources if the goal is prediction. In this paper we describe a fully Bayesian treatment of the original HME model based on variational inference. By combining `local' and `global' variational methods we obtain a rigorous lower bound on the marginal probability of the data under the model. This bound is optimized during the training phase, and its resulting value can be used for model order selection. We present results using this approach for data sets describing robot arm kinematics.
Links To Paper
1st Link
Bibtex format
author = { Markus Svensen and Christopher Bishop },
title = {Bayesian Hierarchical Mixtures of Experts},
book title = {Proceedings of UAI 2003 (Conference on Uncertainty in Artificial Intelligence)},
publisher = {Morgan Kaufmann},
year = 2003,
pages = {57-64},
url = {},

Home : Publications : Report 

Please mail <> with any changes or corrections.
Unless explicitly stated otherwise, all material is copyright The University of Edinburgh