(1.7 hours to learn)
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.
This concept has the prerequisites:
Core resources (read/watch one of the following)
→ 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
- create concept: shift + click on graph
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- remove concept from graph: click on concept then press delete/backspace
- add associated content to concept: click the small circle that appears on the node when hovering over it
- other actions: use the icons in the upper right corner to optimize the graph placement, preview the graph, or download a json representation