On the public citation graph

Sparse Bayesian Identification of Arx Transfer Functions for Modal Analysis with Uncertainty Quantification

0 signed · 13 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/stc.2343openalex-snapshot

Sparse Bayesian learning for structural damage detection using expectation–maximization technique

inferredcites →https://doi.org/10.1016/j.engstruct.2022.114001openalex-snapshot

Uncertainty quantification of modal parameter estimates obtained from subspace identification: An experimental validation on a laboratory test of a large-scale wind turbine blade

inferredcites →https://doi.org/10.1016/j.ifacsc.2025.100299openalex-snapshot

A sparse approach to transfer function estimation via Least Absolute Shrinkage and Selection Operator

inferredcites →https://doi.org/10.1016/j.istruc.2023.03.101openalex-snapshot

An improved stochastic subspace modal identification method considering uncertainty quantification

inferredcites →https://doi.org/10.1016/j.jobe.2024.109007openalex-snapshot

A bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings

inferredcites →https://doi.org/10.1016/j.ymssp.2015.04.018openalex-snapshot

Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications

inferredcites →https://doi.org/10.1016/j.ymssp.2020.106701openalex-snapshot

Laplace approximation in sparse Bayesian learning for structural damage detection

inferredcites →https://doi.org/10.1016/j.ymssp.2020.107338openalex-snapshot

Uncertainty quantification in data-driven stochastic subspace identification

inferredcites →https://doi.org/10.1016/j.ymssp.2021.108290openalex-snapshot

Development of a structural identification methodology with uncertainty quantification through the SSI and bootstrap techniques

inferredcites →https://doi.org/10.1016/j.ymssp.2023.110483openalex-snapshot

A robust sparse Bayesian learning method for the structural damage identification by a mixture of Gaussians

inferredcites →https://doi.org/10.1109/tsp.2004.831016openalex-snapshot

Sparse Bayesian Learning for Basis Selection

inferredcites →https://doi.org/10.1111/mice.12358openalex-snapshot

Full Gibbs Sampling Procedure for Bayesian System Identification Incorporating Sparse Bayesian Learning with Automatic Relevance Determination

inferredcites →https://doi.org/10.1177/1369433218811540openalex-snapshot

State-of-the-art review on Bayesian inference in structural system identification and damage assessment

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.