Technical

A short and practical guide to efficiently computing the Cholesky decomposition of matrices perturbed by low-rank updates

One weird trick to make exact inference in Bayesian logistic regression tractable.

This series explores market data provided by official API from Binance, one of the world’s largest cryptocurrency exchanges, using Python. In this post we examine various useful ways to visualize the recent and historical trades.

A short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.

This series explores market data provided by official API from Binance, one of the world’s largest cryptocurrency exchanges, using Python. In this post we examine various useful ways to visualize the orderbook.

A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.

This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.

We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.

An in-depth practical guide to variational encoders from a probabilistic perspective.

The meshgrid function is useful for creating coordinate arrays to vectorize function evaluations over a grid. Experienced NumPy users will have noticed some discrepancy between meshgrid and the mgrid, a function that is used just as often, for exactly the same purpose.