mxfusion.inference.variational¶
Members¶
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class
mxfusion.inference.variational.
VariationalInference
(model, posterior, observed)¶ Bases:
mxfusion.inference.inference_alg.InferenceAlgorithm
The base class for Variational Inference (VI) algorithms.
Parameters: -
posterior
¶ return the variational posterior.
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class
mxfusion.inference.variational.
VariationalSamplingAlgorithm
(model, posterior, observed, num_samples=1, target_variables=None)¶ Bases:
mxfusion.inference.inference_alg.SamplingAlgorithm
The base class for the sampling algorithms that are applied to the models with variational approximation.
Parameters: - model (Model) – the definition of the probabilistic model
- posterior – the definition of the variational posterior of the probabilistic model
- posterior – Posterior
- observed ([Variable]) – A list of observed variables
- num_samples (int) – the number of samples used in estimating the variational lower bound
- target_variables ([UUID]) – (optional) the target variables to sample
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posterior
¶ return the variational posterior.
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class
mxfusion.inference.variational.
StochasticVariationalInference
(num_samples, model, posterior, observed)¶ Bases:
mxfusion.inference.variational.VariationalInference
The class of the Stochastic Variational Inference (SVI) algorithm.
Parameters: - num_samples (int) – the number of samples used in estimating the variational lower bound
- model (Model) – the definition of the probabilistic model
- posterior – the definition of the variational posterior of the probabilistic model
- posterior – Posterior
- observed ([Variable]) – A list of observed variables
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compute
(F, variables)¶ Compute the inference algorithm
Parameters: - F (Python module) – the execution context (mxnet.ndarray or mxnet.symbol)
- variables – the set of MXNet arrays that holds the values of
variables at runtime. :type variables: {str(UUID): MXNet NDArray or MXNet Symbol} :returns: the outcome of the inference algorithm :rtype: mxnet.ndarray.ndarray.NDArray or mxnet.symbol.symbol.Symbol