swh:1:snp:636001a7d28e32091113da06cec462052713b3b6
Tip revision: 235853956f699c32dcc9ce4c4311724c3f90705a authored by st-- on 15 April 2020, 12:19:00 UTC
Release 2.0.1 (#1436)
Release 2.0.1 (#1436)
Tip revision: 2358539
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>