In order to do exact probabilistic inference on real life network efficiently, one must find a good Tree Decomposition of the network. This process is known as the Junction Tree algorithm. It's a bit hard to visualize the result, but while browsing tree decomposition
graph pages on Wikipedia, I got an idea. Instead of bags with variables, we plot it as a collection of colored strips where each strip corresponds to a variable and Running Intersection property guarantees there will be no breaks.
Here's the result for the width-7 tree decomposition of the moralized
Barley network
Mathematica source
6 comments:
Very cool. I like it.
Thanks! I do wonder when the tree decomposition captures the difficulty of exact solution...what kind of problems can your combinatorial MAP approach solve?
THANKS FOR THE INFORMATION...
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Excellent machine learning blog,thanks for sharing...
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Interesting exploration of tree decompositions! Understanding the graph structure is crucial for various algorithms. Has anyone considered using a tree decomposition to optimize solving puzzles like in Infinite Craft? Building from Tree + House = Treehouse, then Treehouse + Dream = Utopia, maybe this logic can be mirrored in graph traversal for faster solutions! More examples and practical applications would be great.
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