5 citations · 8 across the 5 of their papers we have counts for
5 papers · 1 filter
Justifying the Principle of Interval Constraints
Richard E. Neapolitan, James Kenevan
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…
Computation of Variances in Causal Networks
Richard E. Neapolitan, James Kenevan
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…
Investigation of Variances in Belief Networks
Richard E. Neapolitan, James Kenevan
The belief network is a well-known graphical structure for representing independences in a joint probability distribution. The methods, which perform probabilistic inference in bel…
An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks
Peter Che, Richard E. Neapolitan, James Kenevan +1
In recent years the belief network has been used increasingly to model systems in Al that must perform uncertain inference. The development of efficient algorithms for probabilisti…
The Cognitive Processing of Causal Knowledge
Scott B. Morris, Doug Cork, Richard E. Neapolitan
There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learning causal structure using Bayesian networks. It is then argued that…