@inbook{b4cb679d1bad431aa6ab565b0374600a,
title = "A graphical framework for stochastic model-based diagnosis",
abstract = "Diagnosing systems with uncertainty has significant practical importance. Many different methods for performing diagnostics inference on stochastic systems have been developed in fields such as FDI and AI. We provide a factor graph framework that integrates several of these approaches for diagnosing stochastic systems. This integration provides several advantages, e.g., showing inter-relationships among the inference algorithms, a computational toolbox for solving diagnostics problems, and an a priori means for predicting inference complexity based solely on the graph structure.",
author = "Gregory Provan",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 3rd Conference on Control and Fault-Tolerant Systems, SysTol 2016 ; Conference date: 07-09-2016 Through 09-09-2016",
year = "2016",
month = nov,
day = "8",
doi = "10.1109/SYSTOL.2016.7739809",
language = "English",
series = "Conference on Control and Fault-Tolerant Systems, SysTol",
publisher = "IEEE Computer Society",
pages = "566--571",
editor = "Ramon Sarrate",
booktitle = "2016 3rd Conference on Control and Fault-Tolerant Systems, SysTol 2016 - Conference Proceedings",
address = "United States",
}