  We introduce a framework for analyzing transductive combination of Gaussian
process (GP) experts, where independently trained GP experts are combined in a
way that depends on test point location, in order to scale GPs to big data. The
framework provides some theoretical justification for the generalized product
of GP experts (gPoE-GP) which was previously shown to work well in practice but
lacks theoretical basis. Based on the proposed framework, an improvement over
gPoE-GP is introduced and empirically validated.
