Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs

Journal of Computational Physics

Volumn 259, Issue , 2014, Pages 114-134

  Karagiannis, Georgios ^a   Lin, Guang ^a  

^a PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

Author keywords

Bayesian model average; Bayesian model uncertainty; Generalized polynomial chaos; LASSO; MCMC; Median probability model; Splines; Uncertainty quantification

Indexed keywords

BAYESIAN NETWORKS; FUNCTION EVALUATION; INTERPOLATION; MARKOV PROCESSES; MONTE CARLO METHODS; POLYNOMIAL APPROXIMATION; REGRESSION ANALYSIS; STOCHASTIC MODELS; UNCERTAINTY ANALYSIS;

BAYESIAN MODEL AVERAGE; BAYESIAN MODEL UNCERTAINTY; BAYESIAN MODELLING; GENERALIZED POLYNOMIAL CHAOS; GENERALIZED POLYNOMIALS; LASSO; MARKOV CHAIN MONTE CARLO; MARKOV CHAIN MONTE-CARLO; MEDIAN PROBABILITY MODEL; MODELING UNCERTAINTIES; POLYNOMIAL CHAOS; PROBABILITY MODELLING; UNCERTAINTY QUANTIFICATIONS;

STOCHASTIC SYSTEMS;

EID: 84890257825     PISSN: 00219991     EISSN: 10902716     Source Type: Journal    
DOI: 10.1016/j.jcp.2013.11.016     Document Type: Article

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