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Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints
Omar Z Khan · Pascal Poupart · John Agosta

Tue Dec 13 08:45 AM -- 02:59 PM (PST) @

In this paper, we derive a method to refine a Bayes network diagnostic model by exploiting constraints implied by expert decisions on test ordering. At each step, the expert executes an evidence gathering test, which suggests the test's relative diagnostic value. We demonstrate that consistency with an expert's test selection leads to non-convex constraints on the model parameters. We incorporate these constraints by augmenting the network with nodes that represent the constraint likelihoods. Gibbs sampling, stochastic hill climbing and greedy search algorithms are proposed to find a MAP estimate that takes into account test ordering constraints and any data available. We demonstrate our approach on diagnostic sessions from a manufacturing scenario.

Author Information

Omar Z Khan (University of Waterloo)
Pascal Poupart (University of Waterloo)
John Agosta (Microsoft)
John Agosta

John Mark Agosta leads a team that is expanding the machine learning and artificial intelligence capabilities of Microsoft Azure. He joined Microsoft late in his career -- a career that involved working with startups and labs in the Bay Area, in such areas as "The Connected Car 2025" at Toyota ITC, sales opportunity scoring at Inside Sales, malware detection at Intel, and automated planning at SRI. At Intel Labs, he was awarded a Santa Fe Institute Business Fellowship in 2007. He has over 30 peer-reviewed publications and 6 accepted patents. His dedication to probability and its applications is shown by his participation in the annual Uncertainty in AI conference since its inception in 1985. When feeling low he recharges his spirits by singing Russian music with Slavyanka, the Bay Area's Slavic music chorus.

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