Method, program product, and apparatus for generating analysis model
First Claim
1. An analysis model generating method for generating an analysis model in which causal relationships between variables are expressed using a Bayesian network, the method comprising:
- selecting a child variable to be added to the analysis model;
selecting a plurality of variable candidates to be added to the analysis model as parent variables in causal relationships with the child variable;
judging whether each of values indicating the causal relationships between the selected variable candidates and the child variable is high or low, based on the causal relationships between the variable candidates and the child variable;
generating an aggregated variable different from the variable candidates by aggregating a plurality of low causal variable candidates, each of the low causal variable candidates being judged to have a low value in indicating the causal relationship; and
putting high causal variable candidate and the aggregated variable into the analysis model as the parent variables to the child variable, the high causal variable candidate being judged to have a high value in indicating the causal relationship.
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Abstract
The present invention provides an analysis model generating method for generating an analysis model expressed using a Bayesian network. A analysis model generating method includes selecting a child variable to be added to the analysis model; selecting a plurality of variable candidates to be added to the analysis model as parent variables in causal relationships with the child variable; judging whether each of values indicating the causal relationships between the selected variable candidates and the child variable is high or low, based on the causal relationships between the variable candidates and the child variable; generating an aggregated variable different from the variable candidates by aggregating a plurality of low causal variable candidates; and putting high causal variable candidate and the aggregated variable into the analysis model as the parent variables to the child variable.
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Citations
9 Claims
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1. An analysis model generating method for generating an analysis model in which causal relationships between variables are expressed using a Bayesian network, the method comprising:
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selecting a child variable to be added to the analysis model; selecting a plurality of variable candidates to be added to the analysis model as parent variables in causal relationships with the child variable; judging whether each of values indicating the causal relationships between the selected variable candidates and the child variable is high or low, based on the causal relationships between the variable candidates and the child variable; generating an aggregated variable different from the variable candidates by aggregating a plurality of low causal variable candidates, each of the low causal variable candidates being judged to have a low value in indicating the causal relationship; and putting high causal variable candidate and the aggregated variable into the analysis model as the parent variables to the child variable, the high causal variable candidate being judged to have a high value in indicating the causal relationship. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer program product having a computer readable medium including programmed instructions for performing an analysis model generating process to generate an analysis model in which causal relationships between variables are expressed using a Bayesian network, wherein the instructions, when executed by a computer, cause the computer to perform:
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selecting a child variable to be added to the analysis model; selecting a plurality of variable candidates to be added to the analysis model as parent variables in causal relationships with the child variable; judging whether each of values indicating the causal relationships between the selected variable candidates and the child variable is high or low, based on the causal relationships between the variable candidates and the child variable; generating an aggregated variable different from the variable candidates by aggregating a plurality of low causal variable candidates, each of the low causal variable candidates being judged to have a low value in indicating the causal relationship; and putting high causal variable candidates and the aggregated variable into the analysis model as the parent variable to the child variable, the high causal variable candidate being judged to have a high value in indicating the causal relationship.
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9. An analysis model generating apparatus for generating an analysis model in which causal relationships between variables are expressed using a Bayesian network, the apparatus comprising:
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a child variable selecting unit that selects a child variable to be added to the analysis model; a variable candidate selecting unit that selects a plurality of variable candidates to be added to the analysis model as parent variables in causal relationships with the child variable; a causal relationship judging unit that judges whether each of values indicating the causal relationships between the selected variable candidates and the child variable is high or low, based on the causal relationships between the variable candidates and the child variable; an aggregated variable generating unit that generates an aggregated variable different from the variable candidates by aggregating a plurality of low causal variable candidates, each of the low causal variable candidates being judged to have a low value in indicating the causal relationship; and a parent variable determining unit that puts high causal variable candidate and the aggregated variable into the analysis model as the parent variables to the child variable, the high causal variable candidate being judged to have a high value in indicating the causal relationship.
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Specification