A method of learning a structure of a Bayesian network includes computing an ordering of the random variables of the Bayesian network; wherein computing the ordering of the random variables of the Bayesian network is performed by computing an approximate solution to the history dependent traveling salesman problem.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of learning a structure of a Bayesian network, the method comprising: computing an ordering of the random variables of the Bayesian network; wherein computing the ordering of the random variables of the Bayesian network is performed by computing an approximate solution to the history dependent traveling salesman problem. 2. The method of claim 1 wherein: applying the traveling salesman problem algorithm includes applying a Lin-Kernighan heuristic. 3. The method of claim 1 wherein: applying the traveling salesman problem algorithm includes applying a cutting plane method. 4. The method of claim 1 wherein: applying the traveling salesman problem algorithm includes considering random variables of the Bayesian network as cities of a tour and the optimal ordering of random variables as a tour that minimizes overall cost. 5. The method of any claim 1 wherein: applying the traveling salesman problem algorithm includes performing a general k-opt iteration on the Bayesian network. 6. An apparatus for learning a structure of a Bayesian network, the apparatus comprising: a processor; and memory comprising computer-executable instructions that, when executed by the processor, cause the processor to perform operations for learning the structure of the Bayesian network, the operations comprising: computing an ordering of the random variables of the Bayesian network; wherein computing the ordering of the random variables of the Bayesian network is performed by computing an approximate solution to the history dependent traveling salesman problem. 7. The apparatus of claim 6 wherein: applying the traveling salesman problem algorithm includes applying a Lin-Kernighan heuristic. 8. The method of claim 6 wherein: applying the traveling salesman problem algorithm includes applying a cutting plane method. 9. The apparatus of claim 6 wherein: applying the traveling salesman problem algorithm includes considering random variables of the Bayesian network as cities of a tour and the optimal ordering of random variables as a tour that minimizes overall cost. 10. The apparatus of claim 6 wherein: applying the traveling salesman problem algorithm includes performing a general k-opt iteration on the Bayesian network. 11. A computer program product tangibly embodied on a non-transitory computer readable medium for learning a structure of a Bayesian network, the computer program product including instructions that, when executed by a processor, cause the processor to perform operations comprising: computing an ordering of the random variables of the Bayesian network; wherein computing the ordering of the random variables of the Bayesian network is performed by computing an approximate solution to the history dependent traveling salesman problem. 12. The computer program product of claim 11 wherein: applying the traveling salesman problem algorithm includes applying a Lin-Kernighan heuristic. 13. The computer program product of claim 11 wherein: applying the traveling salesman problem algorithm includes applying a cutting plane method. 14. The computer program product of claim 11 wherein: applying the traveling salesman problem algorithm includes considering random variables of the Bayesian network as cities of a tour and the optimal ordering of random variables as a tour that minimizes overall cost. 15. The computer program product of claim 11 wherein: applying the traveling salesman problem algorithm includes performing a general k-opt iteration on the Bayesian network.