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AI (Logic and Reasoning, Machine Learning), extension of propositional…

- - - - it has graphical structure along with probability tables which are associated with every node
        
        top down
        
        prediction
        
        causal reasoning
        
        bottom up
        
        diagnostic reasoning
        
        explanation
      - every bayesian network is directed acyclic graph
        
        qualitative component
        
        The graphical structure
        
        nodes
        
        direction of arrows which shows
        the direction of influence
        
        quantitative compoent
        
        The probabilities
        
        All root nodes will have prior probabilties
        
        All non root/child nodes will have conditional probabilities which is conditioned on the parent node