this doesn't seem very well documented at present.

Description of your problem

Interpolated Docs are missing sample plot. One should be added
https://docs.pymc.io/api/distributions/continuous.html#pymc3.distributions.continuous.Interpolated

Please provide any additional information below.
See example from Normal plot for

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 @JavierAntoran @stratisMarkou,

First of all, thanks for making all of this code available - it's been great to look through!

Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac

Hey! I believe you should reparameterize u and w in the planar flow like they do in the pymc3 (link below) to ensure the planar flow transformation is invertible, see the appendix in the paper https://arxiv.org/abs/1505.05770.

https://github.com/pymc-devs/pymc3/blob/1cdd1631bea48fef8d140e37c3588a8208498ba0/pymc3/variational/flows.py#L374

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

Describe the bug

Default 'name' of MultiplyKernel class is 'add'. It should read 'mul' for consistency with mul method of Kernel class.