https://github.com/GPflow/GPflow
Revision bb08f22e337d1487b8d9ab9944d8b9f7fff853ff authored by Vincent Dutordoir on 18 June 2018, 17:04:06 UTC, committed by Artem Artemev on 18 June 2018, 17:04:06 UTC
* Introduction of MultiOutputFeatures (Mof) and MultiOutputKernels (Mok). These are used to specify a particular setup of multi-output correlation. * Multiple-dispatch for conditional. This allows GPflow to select the most efficient conditional code depending on your choice of Mof and Mok. * Multiple-dispatch for Kuu and Kuf. Previously Kuu(.) and Kuf(.) were member functions of the feature class. This became cumbersome as the calculation of Kuu and Kuf also depends on the kernel used. In line with conditional we now also use multiple-dispatch to calculate Kuu and Kuf for a particular combination of Mok and Mof. * The actual maths to efficiently calculate the output-correlated conditional (credits to @markvdw ) * sample_conditional function that makes sure that the most efficient code is used to get a sample from the conditional distribution. * Minor: we updated a couple of models to use the new multi-output conditional.
1 parent 6baeb43
Tip revision: bb08f22e337d1487b8d9ab9944d8b9f7fff853ff authored by Vincent Dutordoir on 18 June 2018, 17:04:06 UTC
Multi-output conditionals (#724)
Multi-output conditionals (#724)
Tip revision: bb08f22
MANIFEST.in
include doc/source/conf.py
include doc/source/*.rst
include doc/source/notebooks/*.ipynb
include README.md
include RELEASE.md
include LICENSE
include gpflow/gpflowrc
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