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view toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/quickscore_inf_engine.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function engine = quickscore_inf_engine(inhibit, leak, prior) % QUICKSCORE_INF_ENGINE Exact inference for the QMR network % engine = quickscore_inf_engine(inhibit, leak, prior) % % We create an inference engine for QMR-like networks. % QMR is a bipartite graph, where the top layer contains hidden disease nodes, % and the bottom later contains observed finding nodes. % The diseases have Bernoulli CPDs, the findings noisy-or CPDs. % The original QMR (Quick Medical Reference) network has specific parameter values which we are not % allowed to release, for commercial reasons. % % inhibit(f,d) = inhibition probability on f->d arc for disease d, finding f % If inhibit(f,d) = 1, there is effectively no arc from d->f % leak(j) = inhibition prob. on leak node -> finding j arc % prior(i) = prob. disease i is on % % We use exact inference, which takes O(2^P) time, where P is the number of positive findings. % For details, see % - Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI 89. % - Rish and Dechter, "On the impact of causal independence", UCI tech report, 1998. % Note that this algorithm is numerically unstable, since it adds a large number of positive and % negative terms and hopes that some of them exactly cancel. % % For an interesting variational approximation, see % - Jaakkola and Jordan, "Variational probabilistic inference and the QMR-DT network", JAIR 10, 1999. % % See also % - "Loopy belief propagation for approximate inference: an empirical study", % K. Murphy, Y. Weiss and M. Jordan, UAI 99. engine.inhibit = inhibit; engine.leak = leak; engine.prior = prior; % store results here between enter_evidence and marginal_nodes engine.post = []; engine = class(engine, 'quickscore_inf_engine'); % not a child of the inf_engine class!