We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.
Aboleth is a minimalistic TensorFlow framework for scalable Bayesian deep learning and Gaussian process approximation.
Determinant is a software service that makes predictions from sparse data, and learns what data it needs to optimise its performance.
Primary PhD research topic: Expanding the scope and applicability of variational inference to encompass implicit probabilistic models.
An in-depth practical guide to variational encoders from a probabilistic perspective.
Revrand is a full-featured Python library for Bayesian generalized linear models, with random basis kernels for large-scale Gaussian process approximations.