Conditional probability(条件概率) Conditional or posterior probabilities(后验概率)P(alb) e.g.,P(cavity toothache)=0.8 i.e.,given that toothache is all I know Notation for conditional distributions(条件概率分布)： P(Cavity Toothache)=a 2 x 2 matrix of values If we know more,e.g.,cavity is also given,then we have P(cavity toothache,cavity)=1 New evidence may be irrelevant,allowing simplification,e.g., P(cavity toothache,sunny)=P(cavity toothache)=0.8 This kind of inference,sanctioned by domain knowledge,is crucial 口卡B·三4色进分双0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditional probability（条件概率） Conditional or posterior probabilities（后验概率）P(a|b) e.g., P(cavity|toothache) = 0.8 i.e., given that toothache is all I know Notation for conditional distributions（条件概率分布）: P(Cavity|Toothache) = a 2 × 2 matrix of values If we know more, e.g., cavity is also given, then we have P(cavity|toothache, cavity) = 1 New evidence may be irrelevant, allowing simplification, e.g., P(cavity|toothache,sunny) = P(cavity|toothache) = 0.8 This kind of inference, sanctioned by domain knowledge, is crucial