Latent Gaussian Process Regression
This topic contains 0 replies, has 1 voice, and was last updated by arXiv 1 year, 4 months ago.

Latent Gaussian Process Regression
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of nonstationary multimodal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training data. We show how our approach can be used to model multimodal and nonstationary processes. We exemplify the approach on a set of synthetic data and provide results on real data from motion capture and geostatistics.
Latent Gaussian Process Regression
by Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
https://arxiv.org/pdf/1707.05534v2.pdf
You must be logged in to reply to this topic.