A
- ABC-SubSim 25, 30, 33–37
- Aleatory uncertainty 4, 24
- Approximate Bayesian Computation 25
B
- Bayes posterior 61, 68, 74–76
- Bayes’ theorem 8, 10, 13–16, 18, 20, 22, 25, 26, 33, 64, 65
- Bayesian inference 134
- Bayesian inverse problem 3, 4, 14, 17–19, 24–26, 113–115, 126, 131
- Bayesian inversion 160, 182
- Bayesian model class selection 19, 20
- Bayesian modelling 62, 65
- Bayesian Ockham razor 80, 82, 83, 92, 111
- Bayesian processors 44, 55
C
- Complex structures 122
- Conditional expectation 61, 65, 66, 68, 70, 71
- Conditional mean 76
E
- End-of-life calculation 39
- Epistemic uncertainty 4, 5, 8, 24
F
- Failure prognostics 58
- Field identification 155–157, 202
G
- General Polynomial Chaos Expansion (gPCE) 165, 167, 175
M
- Markov Chain Monte Carlo 17, 18, 31
- Metropolis-Hastings 18, 22
- Minimum mean square error estimator (MMSE) 66–76
- Minimum mean squared estimator 202
- Modal analysis 134, 136, 138, 139, 145, 149, 153
- Model updating 85, 87, 88, 110, 133, 134, 139–142, 144, 146, 149–151, 153
- Monte Carlo methods 49
O
- Optimal sensor placement 128
P
- Parameter identification 61, 70, 189, 194
- Particle-filter-based prognostics 56–58
- POD 157, 171, 173, 174, 196, 202
- Polynomial chaos expansion 67
- Posterior probability 26, 29, 30
- Posterior probability 8, 13, 20
- Prior probability 20
- Prognostic performance metrics 41
- R++Remaining useful life calculation 39
S
- Sparse Bayesian learning 79, 81, 83, 84, 88, 105
- Stochastic model updating 133
- Stochastic simulation 25
- Structural dynamics 133
- Structural health monitoring 98, 113, 133
- SubSet simulation 30–35
- System identification 79, 80, 84–88, 95, 106, 133
U
- Ultrasonic guided-waves 113, 114, 118, 124, 126, 130, 131
- Uncertainty analysis 134, 153
- Uncertainty characterization in prognostics 39
- Uncertainty quantification 3, 4, 7, 24
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