RT Journal Article ID 7afcc5192a9e1f33 A1 Najm, Habib N. A1 Berry, Robert D. A1 Safta, Cosmin A1 Sargsyan, Khachik A1 Debusschere, Bert J. T1 DATA-FREE INFERENCE OF UNCERTAIN PARAMETERS IN CHEMICAL MODELS JF International Journal for Uncertainty Quantification JO IJUQ YR 2014 FD 2014-04-17 VO 4 IS 2 SP 111 OP 132 K1 uncertainty quantification K1 data-free inference K1 Bayesian K1 ignition K1 chemistry AB We outline the use of a data-free inference procedure for estimation of uncertain model parameters for a chemical model of methane-air ignition. The method involves a nested pair of Markov chains, exploring both the data and parametric spaces, to discover a pooled joint posterior consistent with available information. We describe the highlights of the method, and detail its particular implementation in the system at hand. We examine the performance of the procedure, focusing on the robustness and convergence of the estimated joint parameter posterior with increasing number of data chain samples. We also comment on comparisons of this posterior with the missing reference posterior density. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,14db5d4c2510c6cc,7afcc5192a9e1f33.html