A library of scalable Bayesian generalised linear models with fancy features
Project description
A library of scalable Bayesian generalised linear models with fancy features
This library implements various Bayesian linear models (Bayesian linear regression) and generalised linear models. A few features of this library are:
A fancy basis functions/feature composition framework for combining basis functions like radial basis function, sigmoidal basis functions, polynomial basis functions etc.
Basis functions that can be used to approximate Gaussian processes with shift invariant covariance functions (e.g. square exponential) when used with linear models [1], [2], [3].
Non-Gaussian likelihoods with Bayesian generalised linear models (GLMs). We infer all of the parameters in the GLMs using auto-encoding variational Bayes [4], and we approximate the posterior over the weights with a mixture of Gaussians, like [5].
Large scale learning using stochastic gradients (Adam, AdaDelta and more).
Scikit Learn compatibility, i.e. usable with pipelines.
Here is an example of approximating a Matern 3/2 kernel with some of our basis functions,
here is an example of the algorithms in revrand approximating a Gaussian Process,
and here is an example of running using our Bayesian GLM with a Poisson likelihood and integer observations,
Have a look at some of the demo notebooks for how we generated these plots, and more!
Quickstart
To install, simply run setup.py:
$ python setup.py installor install with pip:
$ pip install git+https://github.com/nicta/revrand.gitNow have a look at our quickstart guide to get up and running quickly!
Refer to docs/installation.rst for advanced installation instructions.
Useful Links
- Home Page
- Documentation
- Report on the algorithms in revrand
https://github.com/NICTA/revrand/blob/master/docs/report/report.pdf
- Issue tracking
Bugs & Feedback
For bugs, questions and discussions, please use Github Issues.
References
Copyright & License
Copyright 2015 National ICT Australia.
Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
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