https://github.com/GPflow/GPflow
Revision 378d9e8a8ab4bd7e131cdf14b1df07504dcfa2b0 authored by Artem Artemev on 10 July 2019, 10:10:40 UTC, committed by Vincent Dutordoir on 10 July 2019, 10:10:40 UTC
Update docker-image to TF 1.14
1 parent 2970c07
Tip revision: 378d9e8a8ab4bd7e131cdf14b1df07504dcfa2b0 authored by Artem Artemev on 10 July 2019, 10:10:40 UTC
Upate Dockerfile version in CircleCI (#999)
Upate Dockerfile version in CircleCI (#999)
Tip revision: 378d9e8
GLOSSARY.md
## Glossary
GPflow does not always follow standard Python naming conventions,
and instead tries to apply the notation in the relevant GP papers.\
The following is the convention we aim to use in the code.
---
<dl>
<dt>GPR</dt>
<dd>Gaussian process regression</dd>
<dt>SVGP</dt>
<dd>stochastic variational inference for Gaussian process models</dd>
<dt>Shape constructions [..., A, B]</dt>
<dd>the way of describing tensor shapes in docstrings and comments. Example: <i>[..., N, D, D]</i>, this is a tensor with an arbitrary number of leading dimensions indicated using the ellipsis sign, and the last two dimensions are equal</dd>
<dt>X</dt>
<dd>(and variations like Xnew) refers to input points; always of rank 2, e.g. shape <i>[N, D]</i>, even when <i>D=1</i></dd>
<dt>Y</dt>
<dd>(and variations like Ynew) refers to observed output values, potentially with multiple output dimensions; always of rank 2, e.g. shape <i>[N, P]</i>, even when <i>P=1</i></dd>
<dt>Z</dt>
<dd>refers to inducing points</dd>
<dt>M</dt>
<dd>stands for the number of inducing features (e.g. length of Z)</dd>
<dt>N</dt>
<dd>stands for the number of data or minibatch size in docstrings and shape constructions</dd>
<dt>P</dt>
<dd>stands for the number of output dimensions in docstrings and shape constructions</dd>
<dt>D</dt>
<dd>stands for the number of input dimensions in docstrings and shape constructions</dd>
</dl>
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