Gaussian Process Regression with Soft Inequality and Monotonicity Constraints

arXiv:2404.02873v1 Announce Type: cross
Abstract: Gaussian process (GP) regression is a non-parametric, Bayesian framework to approximate complex models. Standard GP regression can lead to an unbounded model in which some points can take infeasible values. We introduce a new GP method that enforces the physical constraints in a probabilistic manner. This GP model is trained by the quantum-inspired Hamiltonian Monte Carlo (QHMC). QHMC is an efficient way to sample from a broad class of distributions. Unlike the standard Hamiltonian Monte …

abstract arxiv bayesian constraints cs.lg framework inequality math.oc non-parametric parametric process regression standard stat.ml type values