BN: inference by probabilistic logic sampling and stochastic sampling

Subject:

Comments:
: KDD BN subgroup, : : Here are some good references on approximate BN inference (some recent, some not so recent from UAI before it had a regular conference proceedings): : : Probabilistic Logic Sampling (Henrion) : http://www2.sis.pitt.edu/~dsl/UAI/UAI86/Henrion.UAI86.html : http://hss.cmu.edu/html/departments/philosophy/TETRAD/tet3/master.htm : : Stochastic Simulation (Shachter, Peot, Pearl) : http://www2.sis.pitt.edu/~dsl/UAI/UAI89/Shachter2.UAI89.html : http://smi-web.stanford.edu/pubs/SMI_Abstracts/SMI-87-0177.html : : Kutato (Herskovits and Cooper) : [this is what the K in K2 stands for; Kutato was the first system that C&H developed for BN structure learning] : http://smi-web.stanford.edu/pubs/SMI_Abstracts/SMI-90-0305.html : : Survey paper on BN inference at CiteSeer : http://citeseer.nj.nec.com/chrisman98roadmap.html : : A really good JAIR paper from this past fall on importance sampling in large BNs (CPCS, Pathfinder, ANDES): : http://www.cs.cmu.edu/afs/cs.cmu.edu/project/jair/pub/volume13/cheng00a-html/ : : More to follow, but please look into these, especially the JAIR paper. : : -Bill

Optional Link URL:
Link Title:
Optional Image URL: