Revrand — Scalable Bayesian Generalised Linear Models

Date

Revrand - Scalable Bayesian Generalised Linear Models

Regression and GLM Demo
revrand uses recent advances in large scale kernel methods to approximate kernel machines, such as Gaussian processes, with linear models. Using this technology we can harness the inferential power of kernel machines while exploiting the scalability of linear models.
GLM Demo
revrand also uses recent advances in variational inference to accurately approximate fully Bayesian posteriors for non-conjugate models, such as generalised linear models. In this way it can provide comprehensive measures of uncertainty in its predictions.

I am the project creator and primary contributor to revrand, a software library implements Bayesian linear models (Bayesian linear regression) and generalised linear models. A few features of this library are:

  • A basis functions/feature composition framework for combining basis functions like radial basis functions, 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.
  • Non-Gaussian likelihoods with Bayesian generalised linear models using a modified version of the nonparametric variational inference algorithm with large scale learning using stochastic gradients (ADADELTA, Adam and others).

The project page is on github.