ROBUST IMPORTANCE SAMPLING FOR BAYESIAN MODEL CALIBRATION WITH SPATIOTEMPORAL DATA | Zendy

Kyle Neal | Zendy; Benjamin Schroeder | Zendy; Joshua Mullins | Zendy; Abhinav Subramanian | Zendy; Sankaran Mahadevan | Zendy

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ROBUST IMPORTANCE SAMPLING FOR BAYESIAN MODEL CALIBRATION WITH SPATIOTEMPORAL DATA

Author(s) -

Kyle Neal,

Benjamin Schroeder,

Joshua Mullins,

Abhinav Subramanian,

Sankaran Mahadevan

Publication year - 2021

Publication title -

international journal for uncertainty quantification

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.664

H-Index - 21

eISSN - 2152-5099

pISSN - 2152-5080

DOI - 10.1615/int.j.uncertaintyquantification.2021033499

Subject(s) - computer science , markov chain monte carlo , robustness (evolution) , sampling (signal processing) , bayesian inference , algorithm , bayesian probability , inference , singular value decomposition , calibration , importance sampling , surrogate model , monte carlo method , data mining , mathematical optimization , machine learning , artificial intelligence , mathematics , statistics , biochemistry , chemistry , filter (signal processing) , computer vision , gene

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