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...Cs advanced topics in ai lecture probabilistic graphical models milos hauskrecht pitt edu sennott square modeling uncertainty with probabilities representing large multivariate distributions directly and exhaustively is hopeless the number of parameters exponential random variables inference can be breakthrough late s beginning bayesian belief networks give solutions to space acquisition bottlenecks partial for time complexities aim alleviate representational computational idea take advantage structure more specifically independences conditional that hold among two classes asymmetric causal effects dependencies markov fields symmetric used often model spatial dependences image analysis bbns represent full joint distribution over compactly using a smaller marginal b are independent p conditionally given c general components e directed acyclic graph nodes correspond missing links encode j m local probability every variable parent configuration x pa t f i where stand parents network burgl...