Matches in SemOpenAlex for { <https://semopenalex.org/work/W2909923913> ?p ?o ?g. }
- W2909923913 abstract "This paper presents an approach for constrained Gaussian Process (GP) regression where we assume that a set of linear transformations of the process are bounded. It is motivated by machine learning applications for high-consequence engineering systems, where this kind of information is often made available from phenomenological knowledge. We consider a GP $f$ over functions on $mathcal{X} subset mathbb{R}^{n}$ taking values in $mathbb{R}$, where the process $mathcal{L}f$ is still Gaussian when $mathcal{L}$ is a linear operator. Our goal is to model $f$ under the constraint that realizations of $mathcal{L}f$ are confined to a convex set of functions. In particular, we require that $a leq mathcal{L}f leq b$, given two functions $a$ and $b$ where $a < b$ pointwise. This formulation provides a consistent way of encoding multiple linear constraints, such as shape-constraints based on e.g. boundedness, monotonicity or convexity. We adopt the approach of using a sufficiently dense set of virtual observation locations where the constraint is required to hold, and derive the exact posterior for a conjugate likelihood. The results needed for stable numerical implementation are derived, together with an efficient sampling scheme for estimating the posterior process." @default.
- W2909923913 created "2019-01-25" @default.
- W2909923913 creator A5008208282 @default.
- W2909923913 date "2019-01-10" @default.
- W2909923913 modified "2023-09-27" @default.
- W2909923913 title "Gaussian processes with linear operator inequality constraints" @default.
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