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Training Bayesian Networks: Several Scenarios

• Scenario 1: Given both the network structure and all variables observable: compute only the CPT entries
• Scenario 2: Network structure known, some variables hidden: gradient descent (greedy hill-climbing) method, i.e., search for a solution along the steepest descent of a criterion function
• Weights are initialized to random probability values
• At each iteration, it moves towards what appears to be the best solution at the moment, w.o. backtracking
• Weights are updated at each iteration & converge to local optimum
• Scenario 3: Network structure unknown, all variables observable: search through the model space to reconstruct network topology
• Scenario 4: Unknown structure, all hidden variables: No good algorithms known for this purpose
• D. Heckerman. A Tutorial on Learning with Bayesian Networks. In Learning in Graphical Models, M. Jordan, ed. MIT Press, 1999.