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- James R. Kenevan, Anneliese Amschler Andrews
- Performance
- 1984

- Richard G. Börn, James R. Kenevan
- SIGMETRICS
- 1988

In multicomputer architectures, in which processors communicate through message-passing, the overhead encountered because of the need to relay messages can significantly affect performance. Based upon some simplifying assumptions including the rate at which a processor generates messages being proportional to its current potential utilization, processor… (More)

The belief network is a well-known graphi cal structure for representing independences in a joint probability distribution. The meth ods, which perform probabilistic inference in belief networks, often treat the conditional probabilities which are stored in the network as certain values. However, if one takes ei ther a subjectivistic or a limiting… (More)

- Mohamed El-Ruby, James R. Kenevan, Robert Carlson, Khalid Khalil
- Great Lakes Computer Science Conference
- 1989

In recent years the belief network has been used increasingly to model systems in AI that must perf orm uncertain inf erence. The de velopment of efficient algorithms fo r proba bilistic inf erence in belief networks has been a fo cus of much research in AI. Efficient al gorithms fo r certain classes of belief networks have been developed, but the… (More)

- Richard G. Börn, James R. Kenevan
- Comput. J.
- 1992

The CPU cycles that are stolen to relay messages can significantly affect the performance of a multicomputer system. This degradation in performance in turn affects the overall cost-effectiveness of such a system. This paper compares the cost-effectiveness of four multicomputer architectures that have received a great deal of recent attention: the… (More)

- Tzilla Elrad, N. Krishna Kumar, James R. Kenevan
- SPDP
- 1990

- Richard E. Neapolitan, James R. Kenevan
- ArXiv
- 2011

When knowledge is obtained from a database, it is only possible to deduce confidence intervals for probability values. With confidence intervals replacing point values, the results in the set covering model include interval constraints for the probabilities of mutuall y exclusive and exhaustive explanations. The Principle of Interval Constraints ranks these… (More)

- Richard E. Neapolitan, James R. Kenevan
- ArXiv
- 2011

The causal (belief) network is a well-known graphical structure for representing independencies in a joint probability distribution. The exact methods and the approximation methods, which perform probabilistic inference in causal networks, often treat the conditional probabilities which are stored in the network as certain values. However, if one takes… (More)

- J. Montgomery, Martha W. Evens, James R. Kenevan
- CAINE
- 2001