Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
Modeling Failure Priors and Persistence in Model-Based Diagnosis
Sampath Srinivas
Abstract:
Probabilistic model-based diagnosis computes the posterior probabilities of failure of components from the prior probabilities of component failure and observations of system behavior. One problem with this method is that such priors are almost never directly available. One of the reasons is that the prior probability estimates include an implicit notion of a time interval over which they are specified -- for example, if the probability of failure of a component is 0.05, is this over the period of a day or is this over a week? A second problem facing probabilistic model-based diagnosis is the modeling of persistence. Say we have an observation about a system at time t_1 and then another observation at a later time t_2. To compute posterior probabilities that take into account both the observations, we need some model of how the state of the system changes from time t_1 to t_2. In this paper, we address these problems using techniques from Reliability theory. We show how to compute the failure prior of a component from an empirical measure of its reliability -- the Mean Time Between Failure (MTBF). We also develop a scheme to model persistence when handling multiple time tagged observations.
Keywords: Bayesian networks, reliability theory, priors, persistence.
Pages: 507-514
PS Link:
PDF Link: /papers/95/p507-srinivas.pdf
BibTex:
@INPROCEEDINGS{Srinivas95,
AUTHOR = "Sampath Srinivas ",
TITLE = "Modeling Failure Priors and Persistence in Model-Based Diagnosis",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "507--514"
}


hosted by DSL   •   site info   •   help