Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Edward Meeds, Simon Osindero
We present an inﬁnite mixture model in which each component com- prises 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, multi- modality and overlapping output signals. The work is similar to that by Rasmussen and Ghahramani ; however, we use a full generative model over input and output space rather than just a conditional model. This al- lows 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.