Revision 48270681afc13081094f7f398a1e194c6b07ba9b authored by vdutor on 03 January 2018, 17:44:53 UTC, committed by Mark van der Wilk on 03 January 2018, 17:44:53 UTC
* Outline of new expectations code. * Quadrature code now uses TensorFlow shape inference. * General expectations work. * Expectations RBF kern, not tested * Add Identity mean function * General unittests for Expectations * Add multipledispatch package to travis * Update tests_expectations * Expectations of mean functions * Mean function uncertain conditional * Uncertain conditional with mean_function. Tested. * Support for Add and Prod kernels and quadrature fallback decorator * Refactor expectations unittests * Psi stats Linear kernel * Split expectations in different files * Expectation Linear kernel and Linear mean function * Remove None's from expectations api * Removed old ekernels framework * Add multipledispatch to setup file * Work on PR feedback, not finished * Addressed PR feedback * Support for pairwise xKxz * Enable expectations unittests * Renamed `TimeseriesGaussian` to `MarkovGaussian` and added tests. * Rename some variable, plus note for later test of <x Kxz>_q. * Update conditionals.py Add comment * Change order of inputs to (feat, kern) * Stef/expectations (#601) * adding gaussmarkov quad * don't override the markvogaussian in the quadrature * can't test * adding external test * quadrature code done and works for MarkovGauss * MarkovGaussian with quad implemented. All tests pass * Shape comments. * Removed superfluous autoflow functions for kernel expectations * Update kernels.py * Update quadrature.py
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model_options.rst
========================
The six core models of GPflow
========================
The following table summarizes the six core model options in GPflow.
+----------------------+--------------------------+----------------------------+-----------------------------+
| | Gaussian | Non-Gaussian (variational) | Non-Gaussian |
| | Likelihood | | (MCMC) |
+======================+==========================+============================+=============================+
| Full covariance | :class:`gpflow.models.GPR` | :class:`gpflow.models.VGP` | :class:`gpflow.models.GPMC` |
+----------------------+--------------------------+----------------------------+-----------------------------+
| Sparse approximation | :class:`gpflow.models.SGPR`| :class:`gpflow.models.SVGP` | :class:`gpflow.models.SGPMC` |
+----------------------+--------------------------+----------------------------+-----------------------------+
The GPLVM which adds latent variables is also included (`notebook <notebooks/GPLVM.html>`_).
GP Regression
-------------
.. automodule:: gpflow.models
.. autoclass:: gpflow.models.GPR
Sparse GP Regression
--------------------
See also the documentation of the `derivation <notebooks/SGPR_notes.html>`_.
.. automodule:: gpflow.models
.. autoclass:: gpflow.models.SGPR
Variational Gaussian Approximation
----------------------------------
See also the documentation of the `derivation <notebooks/VGP_notes.html>`_.
.. automodule:: gpflow.models
.. autoclass:: gpflow.models.VGP
Sparse Variational Gaussian Approximation
-----------------------------------------
.. automodule:: gpflow.models
.. autoclass:: gpflow.models.SVGP
Markov Chain Monte Carlo
------------------------
.. automodule:: gpflow.models
.. autoclass:: gpflow.models.GPMC
Sparse Markov Chain Monte Carlo
-------------------------------
.. automodule:: gpflow.models
.. autoclass:: gpflow.models.SGPMC
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