Decision engine and method and applications thereof

A method of creating a decision engine including a Bayesian network, comprising:

• Retrieving data from a client database to form a focus database, wherein the retrieving includes retrieving data from a static customer database and retrieving data from a data stream;
• Employing an expectation maximization algorithm to provide a value to valueless records in the focus database;
• Applying a Pareto learning process to the focus database to form at least two nodes, a set of arcs to be applied between the at least two nodes, a set of states to be applied within each node, and a set of probabilities applicable to the states;
• and
• Applying a learning process to update a structure of the at least two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states.

A method of using a decision engine including a Bayesian network, comprising:

• Retrieving data from a client database and forming a focus database, wherein the retrieving includes retrieving data from a static customer database and retrieving data from a data stream;
• Employing an expectation maximization algorithm to provide a value to valueless records in the focus database;
• Applying a set of initial rules to the focus database to form at least two nodes;
• Applying a first learning process to determine a set of arcs to be applied between the at least two nodes;
• Applying a second learning process to determine a set of states to be applied within each node;
• Applying a third learning process to determine a set of probabilities applicable to the states learned in the second learning process;
• Applying a fourth learning process to update a structure of the at least two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states;
• Applying evidence to at least one of the nodes;
• and
• Updating the structure according to the applied evidence using at least one of the first, second, third, or fourth learning processes.

A computer program, residing on a computer-readable medium, for creating and using a decision engine including a Bayesian network, the computer program comprising instructions for causing a computer to:

• Retrieve data from a client database and form a focus database, wherein the retrieval includes a retrieval of data from a static customer database and a retrieval of data from a data stream;
• Employ an expectation maximization algorithm to provide a value to valueless records in the focus database;
• Apply a set of initial rules to the focus database to form at least two nodes;
• Apply a first learning process to determine a set of arcs to be applied between the at least two nodes;
• Apply a second learning process to determine a set of states to be applied within each node;
• Apply a third learning process to determine a set of probabilities applicable to the states learned in the second learning process;
• and
• Apply a fourth learning process to update a structure of the at least two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states.