Data.Teams.Structure
 Contents Determine conditional independence Structural results for teams
Description
This module implments an automated algortihm to simplify sequential teams. The simplification is based on conditional independences. Conditional independence is checked using the Bayes Ball algorithm http://citeseer.ist.psu.edu/old/399661.html
Synopsis
 module Data.Teams.Graph observations :: Team -> [Node] -> [Node] -> [Node] irrelevant :: Team -> [Node] -> [Node] -> [Node] determined :: Team -> [Node] -> [Node] effective :: Team -> [Node] -> [Node] -> [Node] simplifyAt :: Team -> Node -> Team simplifyOnce :: Team -> Team simplify :: Team -> Team
Documentation
module Data.Teams.Graph
Determine conditional independence
 observations :: Team -> [Node] -> [Node] -> [Node] Source
Requisite observations The observation nodes are thouse nodes in the condition that are marked as visited
 irrelevant :: Team -> [Node] -> [Node] -> [Node] Source
Irrelevant nodes The nodes that have not been visited from their parents are irrelevant
 determined :: Team -> [Node] -> [Node] Source
Functionally determined nodes Nodes that are irrelevant when we want to know about all variable nodes
 effective :: Team -> [Node] -> [Node] -> [Node] Source
Effectively observed nodes All the ancestors of the reward nodes that are functionally determined by conditioned nodes.
Structural results for teams
 simplifyAt :: Team -> Node -> Team Source
The graph restructuring algorithm of the paper.
 simplifyOnce :: Team -> Team Source
Simplify all nodes of the graph once
 simplify :: Team -> Team Source
The graph simplification aglorithm of the paper I believe that this algorithm will always converge. So, I do not stop the loop after a finite number of iterations. If you find an example that does not converge, please let me know.
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