Weighting and Bayes Nets for Rollup of Surveillance Metrics
- Los Alamos National Laboratory
The LANL IKE team proposes that the surveillance metrics for several data stream that are used to detect the same failure mode be weighted. Similarly, the failure mode metrics are weighted to obtain a subsystem metric. E.g., if there n data streams (nodes 1-n), the failure mode (node 0) metric is obtained as M{sub 0} = w{sub 1}M{sub 1} + {hor_ellipsis} + w{sub n}M{sub n}, where {Sigma}{sub i=1}{sup n} w{sub i} = 1. This proposal has been implemented with Bayes Nets using the Netica/IKE software by specifying an appropriate conditional probability table (CPT). This CPT is calculated using the same form as (1), where the data stream metrics for the true (T) and false (F) states are replaced by 1 and 0, respectively. Then using this CPT, the failure mode metric calculated by Netica/IKE equals (1). This result has two nice features. First, the rollup Bayes nets is doing can be easily explained. Second, because Bayes Nets can implement this rollup using Netica/IKE, then data marshalling (allocating next year's budget) can be studied. A proof that the claim 'failure mode metric calculated by Netica/IKE equals (1)' for n = 2 and n = 3 follows as well as the sketch of a proof by induction for general n.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- DOE/LANL
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1039675
- Report Number(s):
- LA-UR-12-20962; TRN: US201210%%131
- Country of Publication:
- United States
- Language:
- English
Similar Records
The Sequential Empirical Bayes Method: An Adaptive Constrained-Curve Fitting Algorithm for Lattice QCD
Possibility of large direct CP violation in B{r_arrow}K{pi}-like modes because of long distance rescattering effects and implications for the angle {gamma}