Regression using non-Gaussian random fields.
Generalised Gaussian process regression.
Is there ever an actual need for this?
Or can we just use mostly-Gaussian process with some non-Gaussian distribution
marginal and pretend, via GP quantile regression, or some variational GP approximation or non-Gaussian likelihood over Guaussian latents.
Presumably if we suspect higher moments than the second are important, or that there is some actual stochastic process that we know matches our phenomenon, we might
bother with this, but oh my it can get complicated.
TO: example, maybe using sparse stochastic process priors,
Neural process regression
Singh et al. (2019),
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Louizos, Christos, Xiahan Shi, Klamer Schutte, and Max Welling. 2019. “The Functional Neural Process.” arXiv:1906.08324 [cs, Stat]
, June. http://arxiv.org/abs/1906.08324
Singh, Gautam, Jaesik Yoon, Youngsung Son, and Sungjin Ahn. 2019. “Sequential Neural Processes.” arXiv:1906.10264 [cs, Stat]
, June. http://arxiv.org/abs/1906.10264
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In 2015 International Conference on Sampling Theory and Applications (SampTA)
, 221–25. https://doi.org/10.1109/SAMPTA.2015.7148884
Unser, M., P. D. Tafti, A. Amini, and H. Kirshner. 2014. “A Unified Formulation of Gaussian Vs Sparse Stochastic Processes - Part II: Discrete-Domain Theory.” IEEE Transactions on Information Theory
60 (5): 3036–51. https://doi.org/10.1109/TIT.2014.2311903
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