teams-0.0.2.1: Graphical modeling tools for sequential teams Source code Contents Index

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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