Learning parameters of special probability structures in bayesian networks

A computer system for data processing, the computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform:

• providing a Bayesian network model including a special model structure;
• learning probabilities between at least one node of the Bayesian network model and a parent node of the Bayesian network model, wherein learning the probabilities is performed by assuming no special model structure is included in the Bayesian network model;
• optimizing parameters that describe learned probabilities of the Bayesian network model including the special model structure;
• and
• updating the Bayesian network model including the special model structure using optimized parameters.

A computer program product for data processing, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to perform:

• providing a Bayesian network model including a special model structure;
• learning probabilities between at least one node of the Bayesian network model and a parent node of the Bayesian network model, wherein learning the probabilities is performed by assuming no special model structure is included in the Bayesian network model;
• optimizing parameters that describe learned probabilities of the Bayesian network model including the special model structure;
• and
• updating the Bayesian network model including the special model structure using optimized parameters.