Non-Gaussian Bayesian functional regression
October 9, 2019 — May 25, 2023
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 Gaussian 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); is that distinct ? See also Q-EP and elliptical processes…