Pages that link to "Item:Q2309095"
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The following pages link to Bayesian system identification based on hierarchical sparse Bayesian learning and Gibbs sampling with application to structural damage assessment (Q2309095):
Displaying 13 items.
- Sequential sparse Bayesian learning with applications to system identification for damage assessment and recursive reconstruction of image sequences (Q2020862) (← links)
- Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs (Q2021040) (← links)
- Parameter identification for phase-field modeling of fracture: a Bayesian approach with sampling-free update (Q2033643) (← links)
- Generalized integral transform and Hamiltonian Monte Carlo for Bayesian structural damage identification (Q2109785) (← links)
- Nonlinear model updating through a hierarchical Bayesian modeling framework (Q2138683) (← links)
- Variational Bayesian approximation of inverse problems using sparse precision matrices (Q2138759) (← links)
- A new adaptive approach of the Metropolis-Hastings algorithm applied to structural damage identification using time domain data (Q2174719) (← links)
- Strategy for sensor number determination and placement optimization with incomplete information based on interval possibility model and clustering avoidance distribution index (Q2184441) (← links)
- Probabilistic model updating via variational Bayesian inference and adaptive Gaussian process modeling (Q2237321) (← links)
- BUAK-AIS: efficient Bayesian updating with active learning kriging-based adaptive importance sampling (Q2670376) (← links)
- Bayesian structural identification using Gaussian process discrepancy models (Q6194153) (← links)
- Towards probabilistic robust and sparsity-free compressive sampling in civil engineering: a review (Q6538371) (← links)
- SBFEM and Bayesian inference for efficient multiple flaw detection in structures (Q6539842) (← links)
