discrete bayesian network ========================= This is an example input file for a Bayesian network with discrete conditional probability distributions. The example is a small (5 node) graph modeling a student's performance. The graph skeleton data is also included, in the vertex set ("V") and the edge set ("E"). The graph itself is from Koller et al. 53.:: { "V": ["Letter", "Grade", "Intelligence", "SAT", "Difficulty"], "E": [["Intelligence", "Grade"], ["Difficulty", "Grade"], ["Intelligence", "SAT"], ["Grade", "Letter"]], "Vdata": { "Letter": { "ord": 4, "numoutcomes": 2, "vals": ["weak", "strong"], "parents": ["Grade"], "children": None, "cprob": { "['A']": [.1, .9], "['B']": [.4, .6], "['C']": [.99, .01] } }, "SAT": { "ord": 3, "numoutcomes": 2, "vals": ["lowscore", "highscore"], "parents": ["Intelligence"], "children": None, "cprob": { "['low']": [.95, .05], "['high']": [.2, .8] } }, "Grade": { "ord": 2, "numoutcomes": 3, "vals": ["A", "B", "C"], "parents": ["Difficulty", "Intelligence"], "children": ["Letter"], "cprob": { "['easy', 'low']": [.3, .4, .3], "['easy', 'high']": [.9, .08, .02], "['hard', 'low']": [.05, .25, .7], "['hard', 'high']": [.5, .3, .2] } }, "Intelligence": { "ord": 1, "numoutcomes": 2, "vals": ["low", "high"], "parents": None, "children": ["SAT", "Grade"], "cprob": [.7, .3] }, "Difficulty": { "ord": 0, "numoutcomes": 2, "vals": ["easy", "hard"], "parents": None, "children": ["Grade"], "cprob": [.6, .4] } } }