90 research outputs found
Getting to know your probabilities: Three ways to frame personal probabilities for decision making
A robust data driven approach to quantifying common-cause failure in power networks.
The standard alpha-factor model for common cause failure assumes symmetry, in that all components must have identical failure rates. In this paper, we generalise the alpha-factor model to deal with asymmetry, in order to apply the model to power networks, which are typically asymmetric. For parameter estimation, we propose a set of conjugate Dirichlet-Gamma priors, and we discuss how posterior bounds can be obtained. Finally, we demonstrate our methodology on a simple yet realistic example
A Robust Data Driven Approach to Quantifying Common-Cause Failure in Power Networks
The standard alpha-factor model for common cause failure assumes symmetry, in that all components must have identical failure rates. In this paper, we generalise the alpha-factor model to deal with asymmetry, in order to apply the model to power networks, which are typically asymmetric. For parameter estimation, we propose a set of conjugate Dirichlet-Gamma priors, and we discuss how posterior bounds can be obtained. Finally, we demonstrate our methodology on a simple yet realistic example
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