# d-separation

(1.7 hours to learn)

## Summary

D-separation gives a way of determining conditional independence properties in Bayes nets in terms of the graph structure. It captures an intuitive notion of the "flow" of probabilistic influence through the graph.

## Context

This concept has the prerequisites:

- Bayesian networks (d-separation is a way of analyzing Bayes nets.)
- conditional independence (d-separation is a way of finding conditional independencies.)

## Core resources (read/watch one of the following)

## -Free-

→ Coursera: Probabilistic Graphical Models (2013)

An online course on probabilistic graphical models.

Other notes:

- Click on "Preview" to see the videos.

## -Paid-

→ Probabilistic Graphical Models: Principles and Techniques

A very comprehensive textbook for a graduate-level course on probabilistic AI.

Location:
Sections 3.3-3.3.2, pages 68-74

→ Pattern Recognition and Machine Learning

A textbook for a graduate machine learning course, with a focus on Bayesian methods.

Location:
Section 8.2, pages 372-383

## See also

- The Markov blanket is another characterization of Bayes nets in terms of conditional independencies.
- The d-separation criterion presented here is inefficient when implemented as an algorithm. The Bayes Ball algorithm gives an efficient way of testing conditional independence.