Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that ...

A five-minute formula from Alexander Denev that takes you through a simple probabilistic graphical model and explains how and why these are used. Find out more about the ground-breaking book, ...

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 ...

Case Study: The Impact of an Interest Rate Hike on Mortgage Default Rates In the previous chapter we showed how to build some simple PGMs that can encode our knowledge about how the real world “works” ...

High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for ...

Graphical models form a cornerstone of modern data analysis by providing a visually intuitive framework to represent and reason about the complex interdependencies among variables. In particular, ...

Probabilistic inference depends exponentially on the so called tree width, which is a measure of the worst-case intermediate result during inference that is bounded from below by the maximum number of ...