This is an awesome library, thanks @ddbourgin!!

Users might not know the best way to install this package and try it out. (I didn't, so I eventually just copied the source files.)
Neither the readme nor readthedocs have install instructions.

I couldn't find it on PyPi or Anaconda, and there doesn't appear to be a pyproject.toml, setup.cfg, setup.py, or conda recipe.

Moreover, the t

Howdy folks,

GPyTorch provides Gaussian likelihood objects for fixed noise (FixedNoiseGaussianLikelihood) and for multi-task models (MultitaskGaussianLikelihood). I was wondering if someone could provide me some guidance on how to get a fixed noise multi-task Gaussian likelihood?

Thanks in advance

Galto

We would like all GPflow kernels to broadcast across leading dimensions. For most of them, this is implemented already (#1308); this issue is to keep track of the ones that currently don't:

• ArcCosine
• Coregion
• Periodic
• ChangePoints
• Convolutional
• all MultioutputKernel subclasses

Hi,

I'm trying to use the RQ kernel. First of all, in its help file I don't understand if I should put alpha or log_alpha as an input parameter. If the latter, is the logarithm natural or base 10?

Second of all, I checked R&W, and their formula 4.19 is not exactly the same as the one in george. Is it an issue, or are they mathematically equivalent?

Thanks in advance

Cheers

Hi,

I am interested in using pykrige.ok.OrdinaryKriging and pykrige.uk.UniversalKriging in many different set ups with respect to the accepted parameters. Thus, it makes sense to use GridSearchCV to define a big dictionary with all the parameters and let the library do the rest. However, it seems that GridSearchCV is coupled to the RegressionKriging package and not accepting parameters fr

Hi,

Thanks for sharing the code.

Is there ant simple "getting started" document on setting up like a basic EI or KG with your tool.
1- I have seen the main.py in the "example" folder, it is good but it is for a very complete case, not for simple sampling mode.
2- I have tried the MOE getting started document (available in the "doc" folder) but as the function names and procedures are chan

The new plotting functionality, courtesy of @nathbo, it great. It's currently undocumented other than in the readme though. Would be good if a page could be added to the docs describing the API and giving a couple of basic usage examples.

@eford pointed out that equation (60) in the paper should have an upper limit of J, not 2J. Not sure if an erratum is warranted...

Create the following documentation pages:

• getting started: intro
• getting started: installation
• getting started: rename basic usage -> quickstart, add a bit on hyperparameters
• advanced usage: basic concepts
• advanced usage: hyperparameters
• advanced usage: tuners -> extend this in depth
• advanced usage: selectors -> extend this in depth

Reorganize tree, see MLBlocks.

See als

Lot of people state that Appveyor is better than travis and now they have linux support.. Maybe at some point we want to change to Appveyor..

Hey guys, for the ub and lb you declare it as a function. But when you pass it later to unit box you declare it in unit box function docu as a np.array. I think the func should just be replaced by np.array

:type lb: function
:param ub: Upper variable bounds, of size 1 x dim
:type ub: function

Cheers :)

From Lu Cheng:

"It was said in GPStuff manual page 42 that periodic kernel was coming
from this paper
http://jmlr.org/proceedings/papers/v33/solin14.pdf

In page 907, equation (23) and GPStuff appendix, there is the canonical
periodic covariance function. And it is not obvious to find the explicit
form of quasi-periodic covariance function in section 3.5.

In the demo_periodic.m, there is alway t