Systems and methods for gaussian process modeling for heterogeneous functions

A method, comprising:

• accessing observation data expressive of operation of an observed system, the observation data including:
• a class label associated with an input example of a plurality of input examples, the class label being one of a plurality of class labels;
• and
• a set of closest boundary information for the input example;
• determining a plurality of partitions of an input space of the observation data for a class label of the plurality of class labels of the observation data for the observed system using the set of closest boundary information;
• and
• determining a heterogeneous function model for the observed system based on the observation data, the heterogeneous function model for the observed system incorporating a class likelihood function for each class label of the plurality of class labels, the class likelihood function incorporating partition-level log marginal likelihoods for the plurality of partitions of the class label.

A non-transitory computer readable medium including instructions encoded thereon that are executable by a processor to:

• access observation data expressive of operation of an observed system, the observation data including:
• a class label associated with an input example of a plurality of input examples, the class label being one of a plurality of class labels;
• and
• a set of closest boundary information for the input example;
• determine a plurality of partitions of an input space of the observation data for a class label of the plurality of class labels of the observation data for the observed system using the set of closest boundary information;
• and
• determine a heterogeneous function model for the observed system based on the observation data, the heterogeneous function model for the observed system incorporating a class likelihood function for each class label of the plurality of class labels, the class likelihood function incorporating partition-level log marginal likelihoods for the plurality of partitions of the class label, and each partition of the plurality of partitions of the class label respectively being associated with a partition-level stationary function modeled using a Gaussian process prior distribution having a continuous stationary kernel, the continuous stationary kernel being common to each partition of the plurality of partitions that share the class label.