@inproceedings{meeds2005an,
author = {Meeds, Ted and Osindero, Simon},
title = {An Alternative Infinite Mixture Of Gaussian Process Experts},
booktitle = {Neural Information Processing Systems},
year = {2005},
month = {December},
abstract = {We present an infinite mixture model in which each component comprises a multivariate Gaussian distribution over an input space, and a Gaussian Process model over an output space. Our model is neatly able to deal with non-stationary covariance functions, discontinuities, multimodality and overlapping output signals. The work is similar to that by Rasmussen and Ghahramani [1]; however, we use a full generative odel
over input and output space rather than just a conditional model. This allows us to deal with incomplete data, to perform inference over inverse functional mappings as well as for regression, and also leads to a more powerful and consistent Bayesian specification of the effective ‘gating network’ for the different experts.},
url = {http://approjects.co.za/?big=en-us/research/publication/alternative-infinite-mixture-gaussian-process-experts/},
edition = {Neural Information Processing Systems},
}