Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.

Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...

We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This ...

This post details the beginning of Bloomberg’s journey to build a machine learning inference platform. For those readers who are less familiar with the technical concepts involved in machine learning ...

The Ising model, when used as a technique, refers to a computational and analytical framework for studying systems of binary variables with pairwise interactions, typically on a lattice or graph, via ...