Abstract: Disclosed is a system and method for predicting political instability. This instability is predicted for specific countries or geographic regions. In one embodiment, the prediction is carried out on a basis of a probabilistic model, such as a Bayesian-network. The model is comprised of various notes corresponding to dependent and independent variables. The independent variables, in turn, correspond to factors relating to historical political instability. The dependent variable corresponds to the prediction of instability. By populating the independent variables with current data, future political instability can be predicted.

Abstract: In accordance with a particular embodiment of the invention, a method for explaining a recommendation produced by a decision support tool is disclosed. The method comprises submitting a list of observation inputs to the decision support tool and producing a recommendation. The list of inputs is then reordered according to an observation metric. The method further comprises quantifying how each input impacts the probability of the recommendation produced. The inputs may then be ranked by comparing the associated changes in probability of the recommendation produced.

Abstract: A method and computer program product to capture expert knowledge and data using probabilistic models. A custom layered structure and nodes reduce the complexity of the model, allowing for representation of the model using tables. An editor is used for entry and verification of expert knowledge and data into tables and a probabilistic model is generated from the tables.

Abstract: A method, apparatus, and computer program product are presented for automatically evaluating Bayesian network models. Operations performed comprise receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes that are linked with the evidence nodes by causal dependency links, and where the evidence nodes have evidence states and the conclusion nodes have conclusion states. The states of conclusion nodes are set to desired conclusion states and corresponding probabilities of occurrence of evidence states are determined by propagating these states down the causal dependency links. Thus, samples of most likely states of the evidence nodes are generated. Then, states of the evidence nodes are set corresponding to the samples of the evidence states. These states are propagated back up the causal dependency links to obtain probabilities of the resulting states of the conclusion nodes. Finally, a representation is outputted for the probabilities of the states of the conclusion nodes.

Abstract: In accordance with a particular embodiment of the invention, a method for explaining a recommendation produced by a decision support tool is disclosed. The method comprises submitting a list of observation inputs to the decision support tool and producing a recommendation. The list of inputs is then reordered according to an observation metric. The method further comprises quantifying how each input impacts the probability of the recommendation produced. The inputs may then be ranked by comparing the associated changes in probability of the recommendation produced.

Abstract: Described is a system for diagnosis and prognosis of a component. The system is configured to receive a signal from a component. The signal is representative of a current health observation of the component. The system also computes a present likelihood of the component failure based on the signal. Additionally, the system computes a future likelihood of failure of the component for a given future mission. Through diagnosis, a user can determine the present health of the component, and based on the present health and future mission, determine whether or not the component will fail in the future mission.

Abstract: The present invention converts decision flowcharts into decision probabilistic graphs on a data processing system. First, a decision flowchart is received, having evidence nodes, a root evidence node, and outcome nodes. The outcome nodes are related to the evidence nodes by conclusion links. Next, an operation is performed, generating a probabilistic graph based on the flowchart. The graph includes an aggregate outcome node having outcome states, with each outcome state representing an outcome node of the flowchart; a plurality of test nodes, each matching an evidence node in the flowchart, and each test state matching a conclusion link from the evidence node in the flowchart, and causal links between the aggregate outcome node and the evidence nodes. Prior probabilities are calculated for outcome states based on predetermined likelihoods. Conditional probabilities are determined for test states by examining dependencies of conclusion links on the outcome nodes in the decision flowchart.

Abstract: A method, apparatus and computer program product for conversion of decision trees into probabilistic models such as Bayesian networks. Decisions trees are converted into probabilistic models without loss of information stored in the decision tree implicitly or explicitly. As a result, the probabilistic model is usable to reproduce the paths of the original tree. An inference algorithm can be used to reproduce the paths of the original tree from the probabilistic model.

Abstract: Fault trees are automatically converted to Bayesian networks for assisting in system reliability, failure analysis and diagnostics by using information from the fault tree structure to create the Bayesian network structure, creating parameters of the Bayesian network using information from the fault tree, obtaining information about observation nodes for the Bayesian network from a list of observations that augments information contained in the fault tree, and inserting the observation nodes into the Bayesian network. The fault tree is pre-processed into an intermediate format prior to conversion that may include adding reliability values from a separate text document when the fault tree is in such format that requires it.

Abstract: A data processor intended for a single instruction, multiple data mode operation includes memory that is external to the processor array, and a controller that dynamically and selectably interconnects multiple edges of the processor array with the memory and with I/O ports. A separate controller module is provided for each memory channel, and interconnects with corresponding edge processing elements of the processor array. The controller modules for the different channels are independent of each other, as are the channel memories. In the case of a rectangular processor array, each channel memory can be implemented with only three memory stores that are interconnected with the four edges of the processing array and the I/O ports through the channel controller module, yet for most algorithms provide a throughput that is comparable to that resulting from the use of four memory stores.