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A library of scalable Bayesian generalised linear models with fancy features

Project description

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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,

docs/matern32.png

here is an example of the algorithms in revrand approximating a Gaussian Process,

docs/glm_sgd_demo.png

and here is an example of running using our Bayesian GLM with a Poisson likelihood and integer observations,

docs/glm_demo.png

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 install

or install with pip:

$ pip install git+https://github.com/nicta/revrand.git

Now have a look at our quickstart guide to get up and running quickly!

Refer to docs/installation.rst for advanced installation instructions.

Bugs & Feedback

For bugs, questions and discussions, please use Github Issues.

References

[1]Yang, Z., Smola, A. J., Song, L., & Wilson, A. G. “A la Carte – Learning Fast Kernels”. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, pp. 1098-1106, 2015.
[2]Le, Q., Sarlos, T., & Smola, A. “Fastfood-approximating kernel expansions in loglinear time.” Proceedings of the international conference on machine learning. 2013.
[3]Rahimi, A., & Recht, B. “Random features for large-scale kernel machines”. Advances in neural information processing systems. 2007.
[4]Kingma, D. P., & Welling, M. “Auto-encoding variational Bayes”. Proceedings of the 2nd International Conference on Learning Representations (ICLR). 2014.
[5]Gershman, S., Hoffman, M., & Blei, D. “Nonparametric variational inference”. Proceedings of the international conference on machine learning. 2012.

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