Super-node normalized belief propagation for probabilistic systems
June 2, 2020. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent ReferencesProbabilistic resource allocation system with self-adaptive capability Probabilistic resource allocation system with self-adaptive capability Methods and apparatus for building attribute transition probability models for use in pre-fetching resources Method, system, and computer program product for visualizing a data structure Speech recognition with mixtures of bayesian networks Determining signal transduction pathways Clustering with mixtures of bayesian networks Automatic determination of the number of clusters by mixtures of bayesian networks Fast clustering with sparse data Explanation generation system for a diagnosis support tool employing an inference system Patent #: 6601055 InventorsApplicationNo. 09586281 filed on 06/02/2000US Classes:703/2, MODELING BY MATHEMATICAL EXPRESSION703/13, SIMULATING ELECTRONIC DEVICE OR ELECTRICAL SYSTEM706/14, ADAPTIVE SYSTEM706/15, NEURAL NETWORK706/22, Signal processing (e.g., filter)706/45KNOWLEDGE PROCESSING SYSTEMExaminersPrimary: Thomson, WilliamAttorney, Agent or FirmInternational ClassesG06F 1710G06F 716 G06F 1750 AbstractA method determines the probabilities of states of a system represented by a model including of nodes connected by links. Each node represents possible states of a corresponding part of the system, and each link represents statistical dependencies between possible states of related nodes. The nodes are grouped into arbitrary sized clusters such that every node is included in at least one cluster. A minimal number of marginalization constraints to be satisfied between the clusters are determined. A super-node network is constructed so that each cluster of nodes is represented by exactly one super-node. Super-nodes that share one of the marginalization constraints are connected by super-links. The super-node network is searched to locate closed loops of super-nodes containing at least one common node. A normalization operator for each closed loop is determined, and messages between the super-nodes are defined. Initial values are assigned to the messages, and the messages between super-nodes are updated using standard belief propagation. The messages are replaced by associated normalized values using the corresponding normalization operator, and approximate probabilities of the states of the system are determined from the messages when a termination condition is reached. | |
