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48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
__doc__ = """
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Inference over Gaussian process latent functions
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In all our GP models, the consistency propery means that we have a Gaussian
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prior over a finite set of points f. This prior is
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math:: N(f | 0, K)
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where K is the kernel matrix.
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We also have a likelihood (see GPy.likelihoods) which defines how the data are
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related to the latent function: p(y | f). If the likelihood is also a Gaussian,
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the inference over f is tractable (see exact_gaussian_inference.py).
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If the likelihood object is something other than Gaussian, then exact inference
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is not tractable. We then resort to a Laplace approximation (laplace.py) or
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expectation propagation (ep.py).
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The inference methods return a
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:class:`~GPy.inference.latent_function_inference.posterior.Posterior`
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instance, which is a simple
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structure which contains a summary of the posterior. The model classes can then
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use this posterior object for making predictions, optimizing hyper-parameters,
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etc.
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"""
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from exact_gaussian_inference import ExactGaussianInference
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from laplace import Laplace
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from GPy.inference.latent_function_inference.var_dtc import VarDTC
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from expectation_propagation import EP
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from expectation_propagation_dtc import EPDTC
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from dtc import DTC
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from fitc import FITC
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from var_dtc_parallel import VarDTC_minibatch
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from var_dtc_gpu import VarDTC_GPU
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# class FullLatentFunctionData(object):
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#
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#
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# class LatentFunctionInference(object):
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# def inference(self, kern, X, likelihood, Y, Y_metadata=None):
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# """
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# Do inference on the latent functions given a covariance function `kern`,
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# inputs and outputs `X` and `Y`, and a likelihood `likelihood`.
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# Additional metadata for the outputs `Y` can be given in `Y_metadata`.
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# """
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# raise NotImplementedError, "Abstract base class for full inference"
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