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Gaussian Process Regression with Soft Inequality and Monotonicity Constraints
April 4, 2024, 4:42 a.m. | Didem Kochan, Xiu Yang
cs.LG updates on arXiv.org arxiv.org
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
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