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A toolbox for performing variational Bayesian inference

Project description

Variational Bayesian Inference Toolbox


This module is inspired by the paper ‘Black Box Variational Inference’ by Rajesh Ranganath et al. It attempts to make nearly trivial the task of fitting a variational distribution to a user-specified log-likelihood function without derivatives. Currently it only uses a mean field variational distribution, but the main class VariationalInferenceMF is flexible enough for simple subclassing in the future. This module also contains a number of implementations of stochastic gradient descent algorithms to be used for optimization.

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