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GPy/likelihoods/__init__.py
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@ -1,13 +1,24 @@
"""
Introduction
"""Introduction
^^^^^^^^^^^^
The likelihood is :math:`p(y|f,X)` which is how well we will predict target values given inputs :math:`X` and our latent function :math:`f` (:math:`y` without noise). Marginal likelihood :math:`p(y|X)`, is the same as likelihood except we marginalize out the model :math:`f`. The importance of likelihoods in Gaussian Processes is in determining the 'best' values of kernel and noise hyperparamters to relate known, observed and unobserved data. The purpose of optimizing a model (e.g. :py:class:`GPy.models.GPRegression`) is to determine the 'best' hyperparameters i.e. those that minimize negative log marginal likelihood.
The likelihood is :math:`p(y|f,X)` which is how well we will predict
target values given inputs :math:`X` and our latent function :math:`f`
(:math:`y` without noise). Marginal likelihood :math:`p(y|X)`, is the
same as likelihood except we marginalize out the model :math:`f`. The
importance of likelihoods in Gaussian Processes is in determining the
'best' values of kernel and noise hyperparamters to relate known,
observed and unobserved data. The purpose of optimizing a model
(e.g. :py:class:`GPy.models.GPRegression`) is to determine the 'best'
hyperparameters i.e. those that minimize negative log marginal
likelihood.
.. inheritance-diagram:: GPy.likelihoods.likelihood GPy.likelihoods.mixed_noise.MixedNoise
:top-classes: GPy.core.parameterization.parameterized.Parameterized
Most likelihood classes inherit directly from :py:class:`GPy.likelihoods.likelihood`, although an intermediary class :py:class:`GPy.likelihoods.mixed_noise.MixedNoise` is used by :py:class:`GPy.likelihoods.multioutput_likelihood`.
Most likelihood classes inherit directly from
:py:class:`GPy.likelihoods.likelihood`, although an intermediary class
:py:class:`GPy.likelihoods.mixed_noise.MixedNoise` is used by
:py:class:`GPy.likelihoods.multioutput_likelihood`.
"""
@ -22,4 +33,4 @@ from .mixed_noise import MixedNoise
from .binomial import Binomial
from .weibull import Weibull
from .loglogistic import LogLogistic
from .multioutput_likelihood import MultioutputLikelihood
from .multioutput_likelihood import MultioutputLikelihood