Infinite Mixtures of Gaussian Process Experts 论文

摘要

We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Us-ing an input-dependent adaptation of the Dirichlet Process, we imple-ment a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets – thus potentially over-coming two of the biggest hurdles with GP models. Simulations show the viability of this approach. 1