Commit acdb0aaf by Jakob Knollmueller

### some docstrings on KL

parent 03c31669
 ... ... @@ -21,17 +21,23 @@ from .. import utilities class MetricGaussianKL(Energy): """Provides the sampled Kullback-Leibler divergence between a distribution and a metric Gaussian. """Provides the sampled Kullback-Leibler divergence between a distribution and a Metric Gaussian. The Energy object is an implementation of a scalar function including its gradient and metric at some position. A Metric Gaussian is used to approximate some other distribution. It is a Gaussian distribution that uses the Fisher Information Metric of the other distribution at the location of its mean to approximate the variance. In order to infer the mean, the a stochastic estimate of the Kullback-Leibler divergence is minimized. This estimate is obtained by drawing samples from the Metric Gaussian at the current mean. During minimization these samples are kept constant, updating only the mean. Due to the typically nonlinear structure of the true distribution these samples have to be updated by re-initializing this class at some point. Here standard parametrization of the true distribution is assumed. Parameters ---------- mean : Field The current mean of the Gaussian. hamiltonian : Hamiltonian The Hamiltonian of the approximated probability distribution. hamiltonian : StandardHamiltonian The StandardHamiltonian of the approximated probability distribution. n_samples : integer The number of samples used to stochastically estimate the KL. constants : list ... ... @@ -47,15 +53,7 @@ class MetricGaussianKL(Energy): Notes ----- An instance of the Energy class is defined at a certain location. If one is interested in the value, gradient or metric of the abstract energy functional one has to 'jump' to the new position using the `at` method. This method returns a new energy instance residing at the new position. By this approach, intermediate results from computing e.g. the gradient can safely be reused for e.g. the value or the metric. Memorizing the evaluations of some quantities minimizes the computational effort for multiple calls. For further details see: Metric Gaussian Variational Inference (in preparation) """ def __init__(self, mean, hamiltonian, n_sampels, constants=[], ... ...
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