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Abstract:
We present an extension to the Mixture of Experts (ME) model,
where the individual experts are Gaussian Process (GP) regression
models. Using an input-dependent adaptation of the Dirichlet
Process, we implement 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.
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