From 923343dcc268f35029bf7e1bd0296d7897e7c84a Mon Sep 17 00:00:00 2001 From: bobturneruk Date: Thu, 18 Jun 2020 09:36:24 +0100 Subject: [PATCH] latent function inference intro and format --- .../latent_function_inference/__init__.py | 27 ++++++++++++------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/GPy/inference/latent_function_inference/__init__.py b/GPy/inference/latent_function_inference/__init__.py index be4fbde6..5af09442 100644 --- a/GPy/inference/latent_function_inference/__init__.py +++ b/GPy/inference/latent_function_inference/__init__.py @@ -1,23 +1,30 @@ # Copyright (c) 2012-2014, Max Zwiessele, James Hensman # Licensed under the BSD 3-clause license (see LICENSE.txt) -__doc__ = """ +""" +Introduction +^^^^^^^^^^^^ + +Certain :py:class:`GPy.models` can be instanciated with an `inference_method`. This submodule contains objects that can be assigned to `inference_method`. + Inference over Gaussian process latent functions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -In all our GP models, the consistency propery means that we have a Gaussian -prior over a finite set of points f. This prior is +In all our GP models, the consistency property means that we have a Gaussian +prior over a finite set of points f. This prior is: - math:: N(f | 0, K) +.. math:: + N(f | 0, K) -where K is the kernel matrix. +where :math:`K` is the kernel matrix. -We also have a likelihood (see GPy.likelihoods) which defines how the data are -related to the latent function: p(y | f). If the likelihood is also a Gaussian, -the inference over f is tractable (see exact_gaussian_inference.py). +We also have a likelihood (see :py:class:`GPy.likelihoods`) which defines how the data are +related to the latent function: :math:`p(y | f)`. If the likelihood is also a Gaussian, +the inference over :math:`f` is tractable (see :py:class:`GPy.inference.latent_function_inference.exact_gaussian_inference`). If the likelihood object is something other than Gaussian, then exact inference -is not tractable. We then resort to a Laplace approximation (laplace.py) or -expectation propagation (ep.py). +is not tractable. We then resort to a Laplace approximation (:py:class:`GPy.inference.latent_function_inference.laplace`) or +expectation propagation (:py:class:`GPy.inference.latent_function_inference.expectation_propagation`). The inference methods return a :class:`~GPy.inference.latent_function_inference.posterior.Posterior`