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_nodesengine.post = [];engine = class(engine, 'quickscore_inf_engine'); % not a child of the inf_engine class!