1. Representation of CPTs CH 14.3

2. discrete Canonical distribution: standard Deterministic nodes: values computable exactly from parent nodes Noisy-OR relations: Fever = Cold OR Flu OR Malaria OR leak-node –Leak node: covers anything else

3. Continuous random variables Discretization: large & inexact Mixture of parametrized standard PDFs (e.g. Gaussians with mean & variance) Hybrid Bayesian Networks: –BN with both discrete and continuous vars E.g.: P(Buys|Cost), P(Cost|Harvest_quantity,Subsidy_boolean) –Use 2 linear gaussian PDF, one for Subsidy, one for not Subsidy Can use multivariate Gaussian distributions Conditional Gaussian: has Boolean parents Probit distribution: Integral of a Gaussian up to x –A threshold affected by random Gaussian noise Logit distribution: based on the sigmoid function: –E.g., P(buys | c)=1/(1+exp(-2(-c+mean)/sigma))