1. A computer-implemented method of dynamically updating multiple processors in parallel using a computing system having a processor, memory, and data storage subsystems, the computer-implemented method comprising: partitioning an original model state into a plurality of partial models according to a partial distribution scheme; receiving a local copy of a partial original model state into each of a plurality of processors; partitioning incoming streaming data between the plurality of processors according to their partial distribution model; serially processing the partitioned incoming streaming data by each of the plurality of processors according to attributes to achieve a plurality of partial current model states; serially calculating a difference between the partial original model state and the respective partial current model state for each of the attributes for each of the plurality of processors according to a divergence function measured by one of a squared magnitude divergence, an incremental Kullback-Leibler (KL) divergence, or a posterior KL divergence; for each of the attributes for each of the plurality of processors, comparing the calculated differences with a predetermined threshold level; for each of the attributes for each of the plurality of processors, dynamically merging the calculated differences that exceed the predetermined threshold level, where the dynamically merging occurs as frequently as the partial current model state diverges from the partial original model state; and combining the merged calculated differences that exceed the predetermined threshold level with the partial original model states to obtain an updated model state that replaces the original model state.