Embodiments relate to constructing a tree-shaped Bayesian network from variables associated with conditional dependencies in a given data set, the constructing being performed by a plurality of processors in parallel. An aspect includes assigning a plurality of variables as nodes to a respective plurality of processors. Another aspect includes operating the plurality of processors in a parallel manner to determine a correlation for each pair of nodes. Another aspect includes M variables that are randomly selected as primary nodes defining (M+1) sub-trees. Another aspect includes in each sub-tree the plurality of processors are operated in a parallel manner to determine a correlation for the remaining nodes with each of the primary nodes and to allocate each remaining node to one of the (M+1) sub-trees.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for constructing a tree-shaped Bayesian network from variables associated with conditional dependencies in a given data set, the constructing being performed by a plurality of processors in parallel, the method comprising: assigning a plurality of variables as nodes to a respective plurality of processors; and operating the plurality of processors in a parallel manner to determine a correlation for each pair of nodes; wherein: M variables are randomly selected as primary nodes defining (M+1) sub-trees; and in each sub-tree the plurality of processors are operated in a parallel manner to determine a correlation for the remaining nodes with each of the primary nodes and to allocate each remaining node to one of the (M+1) sub-trees. 2. The method according to claim 1, wherein the selecting and determining are iteratively repeated for each sub-tree until all nodes are linked in said tree-shaped Bayesian network. 3. The method according to claim 1, wherein an initial value of M equals 2 and defines 3 sub-trees. 4. The method according to claim 1, wherein a value of M is less than or equal to the square root of the number of variables in the data set. 5. A computer system for constructing a tree-shaped Bayesian network from variables associated with conditional dependencies in a given data set by a plurality of processors in parallel, the computer system comprising: a plurality of processors to which a respective plurality of variables is assigned to as nodes; and a controller for operating the plurality of processors in a parallel manner to determine a correlation for each pair of nodes; wherein the controller is configured to: randomly select M variables as primary nodes defining (M+1) sub-trees; and operate the plurality of processors in a parallel manner to determine a correlation for the remaining nodes with each of the primary nodes and to allocate each remaining node to one of the (M+1) sub-trees in each sub-tree. 6. The computer system according to claim 5, the controller further configured to iteratively repeat selecting and operating for each sub-tree until all nodes are linked in said tree-shaped Bayesian network. 7. The computer system according to claim 5, wherein the controller sets an initial value of M to 2 to define 3 sub-trees. 8. The computer system according to claim 5, wherein the controller sets the value of M to less than or equal to the square root of the number of variables in the data set. 9. A computer program product for constructing a tree-shaped Bayesian network from variables associated with conditional dependencies in a given data set by a plurality of processors in parallel, the computer program product comprising a computer readable medium having computer-readable program code embodied therewith, which when executed by a computer processor, causes the computer processor to implement: assigning a plurality of variables as nodes to a respective plurality of processors; and operating the plurality of processors in a parallel manner to determine a correlation for each pair of nodes; wherein: M variables are randomly selected as primary nodes defining (M+1) sub-trees; and in each sub-tree, the plurality of processors are operated in a parallel manner to determine a correlation for the remaining nodes with each of the primary nodes and to allocate each remaining node to one of the (M+1) sub-trees. 10. The computer program product according to claim 9, wherein the selecting and determining are iteratively repeated for each sub-tree until all nodes are linked in said tree-shaped Bayesian network. 11. The computer program product according to claim 9, wherein an initial value of M equals 2 and defines 3 sub-trees. 12. The computer program product according to claim 9, wherein the value of M is less than or equal to the square root of the number of variables in the data set.