Uncertainty in Artificial Intelligence
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Computation of Variances in Causal Networks
Richard 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, 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 either a subjectivistic or a limiting frequency approach to probability, one can never be certain of probability values. An algorithm for probabilistic inference should not only be capable of reporting the inferred probabilities; it should also be capable of reporting the uncertainty in these probabilities relative to the uncertainty in the probabilities which are stored in the network. In section 2 of this paper a method is given for determining the prior variances of the probabilities of all the nodes. Section 3 contains an approximation method for determining the variances in inferred probabilities.
Pages: 194-203
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PDF Link: /papers/90/p194-neapolitan.pdf
AUTHOR = "Richard Neapolitan and James Kenevan",
TITLE = "Computation of Variances in Causal Networks",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "194--203"

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