2013/02/20 by Sampath Srinivas, Srinivas, Sampath
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Software Reliability and Analysis Research #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.4984
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
arxiv created 2013/02/20 · openalex publication_date 2013/02/20 · arxiv updated 2013/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 t1 and then another observation at a later time t2. 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 t1 to t2. 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.