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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
Close
Hashes for varibayes-0.0.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c584eeca7c8f2fbedc04ba2474e0c0d52978f1e9d668011ac12a9b5d9aaafcaf |
|
MD5 | ff5fd4299f70992298a6f557a4c07a61 |
|
BLAKE2b-256 | d143b7c1431c3efddc0bea84ca0a26e5061e69c71484b9e800ec513bddc7f5d9 |