Bayesian updating and model class selection with subset simulation

DOI10.1016/J.CMA.2017.01.006zbMath1439.62082arXiv1510.06989OpenAlexW1954641401MaRDI QIDQ2309073

Siu-Kui Au, F. A. DiazDelaO, Alfredo Garbuno-Inigo, Isao Yoshida

Publication date: 6 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1510.06989

zbMATH Keywords

Markov chain Monte CarloBayesian inferencemodel updatingsubset simulationBUS

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Reliability and life testing (62N05)

Related Items (9)

Bayesian inversion using adaptive polynomial chaos kriging within subset simulation ⋮ Bayesian updating of failure probability curves with multiple performance functions of nonlinear structural dynamic systems ⋮ Bayesian analysis of rare events ⋮ BUAK-AIS: efficient Bayesian updating with active learning kriging-based adaptive importance sampling ⋮ A robust method for reliability updating with equality information using sequential adaptive importance sampling ⋮ Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling ⋮ Bayesian inference of random fields represented with the Karhunen-Loève expansion ⋮ Bayesian inference with subset simulation: strategies and improvements ⋮ Bayesian updating and marginal likelihood estimation by cross entropy based importance sampling

Uses Software

• ABC-SubSim

Cites Work

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• Accelerated subset simulation with neural networks for reliability analysis
• Gaussian process hyper-parameter estimation using parallel asymptotically independent Markov sampling
• Bayesian analysis of rare events
• Componentwise adaptation for high dimensional MCMC
• Approximate Bayesian Computation by Subset Simulation
• Probability Theory
• TRANSITIONAL ANNEALED ADAPTIVE SLICE SAMPLING FOR GAUSSIAN PROCESS HYPER-PARAMETER ESTIMATION
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications

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