High fidelity mathematical model building with experimental data: A Bayesian approach

Computers and Chemical Engineering

Volumn 32, Issue 4-5, 2008, Pages 971-989

  Blau, Gary ^a   Lasinski, Michael ^a   Orcun, Seza ^a   Hsu, Shuo Huan ^a   Caruthers, Jim ^a   Delgass, Nicholas ^a   Venkatasubramanian, Venkat ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

Bayes' Theorem; Design of experiments (DOE); Markov Chain Monte Carlo (MCMC) sampling; Model discrimination and validation; Nonlinear statistics

Indexed keywords

APPROXIMATION ALGORITHMS; DESIGN OF EXPERIMENTS; DISTRIBUTION FUNCTIONS; ERROR ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; NONLINEAR EQUATIONS; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING;

BAYES' THEOREM; MODEL DISCRIMINATION; NONLINEAR STATISTICS;

BAYESIAN NETWORKS;

APPROXIMATION ALGORITHMS; BAYESIAN NETWORKS; DESIGN OF EXPERIMENTS; DISTRIBUTION FUNCTIONS; ERROR ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; NONLINEAR EQUATIONS; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING;

EID: 38849204808     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2007.04.008     Document Type: Article

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