  Mixtures of Gaussian process experts is a class of models that can
simultaneously address two of the key limitations inherent in standard Gaussian
processes: scalability and predictive performance. In particular, models that
use Dirichlet processes as gating functions permit straightforward
interpretation and automatic selection of the number of experts in a mixture.
While the existing models are intuitive and capable of capturing
non-stationarity, multi-modality and heteroskedasticity, the simplicity of
their gating functions may limit the predictive performance when applied to
complex data-generating processes. Capitalising on the recent advancement in
the dependent Dirichlet processes literature, we propose a new mixture model of
Gaussian process experts based on kernel stick-breaking processes. Our model
maintains the intuitive appeal yet improve the performance of the existing
models. To make it practical, we design a sampler for posterior computation
based on the slice sampling. The model behaviour and improved predictive
performance are demonstrated in experiments using six datasets.
