Abstract: A decision support system comprises receiving a request from a client computer to derive a quality assessment associated with a health care provider of interest, receiving an identification of a user-selected benchmark, determining a comparison range over which data from the data source is to be analyzed, identifying a set of quality measures, generating a first data set of quality measure performance by evaluating the set of quality measures against underlying medical data in a data source filtered by the range, generating a second data set defining an estimated quality measure performance using a probabilistic forecasting model to evaluate the set of quality measures by drawing inferences about the set of quality measures beyond a period of time for which the underlying medical data is available. An overall quality indicator score is computed, based upon a comparison of the first data set and the second data set.

Abstract: A method of identifying network faults includes receiving subscriber statuses of customer premises equipment (CPE) of a communication network represented as a tree having a root node and leaf nodes. Each leaf node corresponds to a CPE. For each CPE, the method includes: (i) determining a conditional probability of the subscriber status of the CPE for each sub-tree in the network tree; (ii) determining a joint probability of the subscriber status of the CPE for every sub-tree in the network tree; (iii) determining a joint probability of the subscriber status of the CPE for every residual tree in the network tree; and (iv) determining a Bayesian posterior probability of a cut at each leaf node, given the subscriber status of the CPE. The method further includes determining a network node status topology indicating node statuses of nodes of the network tree based on the determined Bayesian posterior probabilities.

Abstract: A decision support system comprises receiving a request from a client computer to derive a quality assessment associated with a health care provider of interest, receiving an identification of a user-selected benchmark, determining a comparison range over which data from the data source is to be analyzed, identifying a set of quality measures, generating a first data set of quality measure performance by evaluating the set of quality measures against underlying medical data in a data source filtered by the range, generating a second data set defining an estimated quality measure performance using a probabilistic forecasting model to evaluate the set of quality measures by drawing inferences about the set of quality measures beyond a period of time for which the underlying medical data is available. An overall quality indicator score is computed, based upon a comparison of the first data set and the second data set.

Abstract: A mathematical model to represent multiple role events of a threat scenario, and a methodology to score the events given the data or evidence contained in a knowledge base is provided. The multiple role event can include any action encompassed by an independent clause having multiple syntactical roles (subject, object, location, etc. . . . ) and containing multiple entities (person A, person B, object C, place D, etc. . . . ). Multiple role events are represented as elements of a tensor space, and are scored using tensor operators built from the semantic graph associated with the knowledge base.

Abstract: A covariance-clustering algorithm for partitioning a graph into sub-graphs (clusters) using variations of the pseudo-inverse of the Laplacian matrix (A) associated with the graph. The algorithm does not require the number of clusters as an input parameter and, considering the covariance of the Markov field associated with the graph, algorithm finds sub-graphs characterized by a within-cluster covariance larger than an across-clusters covariance. The covariance-clustering algorithm is applied to a semantic graph representing the simulated evidence of multiple events.

Abstract: A mathematical model to represent multiple role events of a threat scenario, and a methodology to score the events given the data or evidence contained in a knowledge base is provided. The multiple role event can include any action encompassed by an independent clause having multiple syntactical roles (subject, object, location, etc . . . ) and containing multiple entities (person A, person B, object C, place D, etc . . . ). Multiple role events are represented as elements of a tensor space, and are scored using tensor operators built from the semantic graph associated with the knowledge base.

Abstract: Systems, methods and computer implemented tools are provided for performing Markov chain Monte Carlo simulations. Computer implemented tools include a library of MCMC classes that define a core foundation of the MCMC analysis that minimizes knowledge of computer programming and manages the overhead associated with implementing MCMC simulation. Moreover, a user interacting with the various embodiments of the present invention is given numerical control over the problem at issue so that a skilled user can run successful MCMC simulations even where components of the distribution of interest are non-standard.

Abstract: Systems, methods and computer implemented tools are provided for performing Markov chain Monte Carlo simulations. Computer implemented tools include a library of MCMC classes that define a core foundation of the MCMC analysis that minimizes knowledge of computer programming and manages the overhead associated with implementing MCMC simulation. Moreover, a user interacting with the various embodiments of the present invention is given numerical control over the problem at issue so that a skilled user can run successful MCMC simulations even where components of the distribution of interest are non-standard.