|
| clircle wrote:
| This is very nice. Gaussian process regression didn't click for
| me until I thought of the data as a partially observed sample of
| size one from a stochastic process.
|
| Practically, I have a hard time using Gaussian process
| regression. I find regression with splines to be reasonable and
| fast, and I don't have to fret about the nugget parameter or the
| covariance function structure. But I admit GP regression has a
| beautiful theory.
|
| But there is an equivalence between (smoothing) splines and
| certain types of Gaussian process models. [1]
|
| [1] http://pages.stat.wisc.edu/~wahba/ftp1/oldie/kw70bayes.pdf
| nestorD wrote:
| For me the good reason to use gaussian regression is the fact
| that you get an uncertainty on the output.
|
| The big downside is that it takes expert knowledge (to design a
| proper kernel) and a solid implementation (to avoid the various
| numerical problems they can produce) to apply them to practical
| problem. Most implementation either break down very quickly or
| are not flexible enough for my taste.
|
| I have a Rust implementation [0] which tries to help with the
| flexibility aspect but it is still _very_ far from perfect.
|
| [0]: https://github.com/nestordemeure/friedrich
| clircle wrote:
| Yep, uncertainty intervals are definitely easier to get with
| gp regression.
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