1. pick an ordering of the nodes (any ordering will work, but picking a "good" ordering will reduce the size of the resulting Bayesian network -- maximum cardinality search is one heuristic, there are many others; picking the best ordering in NP-hard.)
2. Direct all existing edges from smaller numbered nodes to larger numbered nodes. 3. For each node i, in reverse order from n, n-1, ... 5,4,3: Fully connect all parents of i (directing edges from smaller numbered nodes to larger). Note some parents of i might have been formed during previous iterations. 4. Compute the required conditional probability tables by querying the Markov network. I believe you can find more info in the Neapolitan book and the Pearl book: http://www.amazon.com/exec/obidos/tg/detail/-/0471618403/ http://www.amazon.com/exec/obidos/tg/detail/-/1558604790 Hope this helps. Dave Michael Shigeki Onishi wrote: > Does anyone have resources on how to transform a Markov Network into a > Bayesian Network? > > Thanks, > > Michael Shigeki Onishi > ------------------------------------------------ > Sun Certified Java Developer > Sun Certified Java Programmer >
