Revision as of 13:32, 28 September 2024 edit Joanico (talk \| contribs) 165 edits Remove MatlabGP, see discussion page. Tag: Undo ← Previous edit		Revision as of 10:33, 29 September 2024 edit undo Dr Saves Paul (talk \| contribs) 33 edits →Comparison table Next edit →
Line 548: \| [[Python (programming language)\|Python]] \| {{yes}} \| {{yes\|POD}}{{efn\|name=POD}} \| {{yes\|Sparse}} \| {{yes\|ND}} Line 599: \|[[MATLAB]] \|{{Yes}} \|{{Yes\|Heteroskedastic, VAE, POD}}{{efn\|name=POD\| POD (Proper Orthogonal Decomposition) is a dimensionality reduction technique used in Gaussian Process regression to approximate complex systems by projecting data onto a lower-dimensional subspace, making computations more efficient. It assumes the system is governed by a few dominant modes, making it ideal for problems with clear separability of scales, but less effective when all dimensions contribute equally to the system's behavior.<ref name="Porrello24" />}} ~~\|{{Yes\|Heteroskedastic, VAE, POD}}~~ \|{{Yes\|Sparse}} \|{{Yes\|ND}}

Comparison of Gaussian process software: Difference between revisions