Gaussian processes with monotonicity information
Jaakko Riihimäki, Aki Vehtari ; JMLR W&CP 9:645-652, 2010.
A method for using monotonicity information in multivariate Gaussian process regression and classification is proposed. Monotonicity information is introduced with virtual derivative observations, and the resulting posterior is approximated with expectation propagation. Behaviour of the method is illustrated with artificial regression examples, and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates.