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JCAT: A Bayesian inference

JCat Goals. To develop a fully Bayesian temporal causal modeling system that is both computationally feasible and theoretically soundEliminate common independence assumptions which degrade the resolution of modelsConstruct an environment to allow quick easy use of powerful modeling technologyProvide cutting edge capabilities to users across the DoD.

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JCAT: A Bayesian inference

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    1. JCAT: A Bayesian inference/belief network tool from AFRL Craig.McNamara.ctr@rl.af.mil 315-330-1448

    3. Uncertainty is not Intuitive A disease Infects 1% of the Population There is a test with 99% accuracy. 99% of sick people are positive 99% of well people are negative You test positive What is the probability you are sick?

    4. p(s|+) = p(+|s) * p(s) /p(+)

    5. Behavior of a Mob Guy

    6. Evidence

    7. Usability Standard Bayesian Systems would require 256 parameters to be entered Recursive Noisy OR (RNOR) reduces required user input Reduces the burden of making a high resolution model

    8. Real World Scenario

    9. Summary JCat provides powerful temporal modeling capabilities in an easy to use platform Allows models to be quickly updated and tested Incorporation of real world observations in to predictive models Has been used successfully on real problems with open source information

    10. Questions? Ask me for a demo Craig.McNamara.ctr@rl.af.mil

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