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likelihoods

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bobturneruk 2020-04-24 10:19:36 +01:00
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GPy/likelihoods/__init__.py
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"""
Introduction
^^^^^^^^^^^^
'Likelihood' in this context is a measure of how well a model *f* predicts a dataset *y*. 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`.
"""
from .bernoulli import Bernoulli from .bernoulli import Bernoulli
from .exponential import Exponential from .exponential import Exponential
from .gaussian import Gaussian, HeteroscedasticGaussian from .gaussian import Gaussian, HeteroscedasticGaussian