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Probabilistic Relational Models
Learning Probabilistic Relational Models
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allow approach attribute X.A Chapter class dependency graph class hierarchy compute conditional distribution conditional independence consider construction correlations dataset decision tree define a coherent Definition dependency structure describe descriptive attributes entities evaluate example existence uncertainty foreign-key joins given graph is acyclic ground Bayesian network guaranteed acyclic independence assumptions indicator variable inference instance dependency graph instantiation introduce join indicator variable joint distribution learned models learning algorithm likelihood link uncertainty log-likelihood marginal likelihood methods multinomial distribution node object skeleton partition attributes path dependency graph patient predict probabilistic model probabilistic relational model probability distribution query random variables reference slots reference uncertainty relational database relational schema relational skeleton relational structure sample search algorithm selectivity estimation semantics shows simple slot chain specifies statistical models student subclass indicator subset sufficient statistics topic training set tuple variables type II edges universal foreign-key closure words