id: 05954267 dt: a an: 05954267 au: Higdon, David; Reese, C.Shane; Moulton, J.David; Vrugt, Jasper A.; Fox, Colin ti: Posterior exploration for computationally intensive forward models. so: Brooks, Steve (ed.) et al., Handbook of Markov chain Monte Carlo. Boca Raton, FL: CRC Press (ISBN 978-1-4200-7941-8/hbk; 978-1-4200-7942-5/ebook). Chapman \& Hall/CRC Handbooks of Modern Statistical Methods, 401-418 (2011). py: 2011 pu: Boca Raton, FL: CRC Press la: EN cc: ut: Metropolis algorithm; Markov chain Monte Carlo method ci: li: ab: From the introduction: In a common inverse problem, we wish to infer about an unknown spatial field \$x=(x_1,\dots,x_m)^T\$, given indirect observations \$y=(y_i,\dots,y_n)^T\$. The observations, or data, are linked to the unknown field \$x\$ through some physical system \$y=ζ(x)+ε\$, where \$ζ(x)\$ denotes the physical system and \$ε\$ is an \$n\$-vector of observation errors. Examples of such problems include medical imaging and cosmology. When a forward model, or simulator, of the physical process \$η(x)\$ is available, one can model the data using the simulator \$y=η(x)+e\$, where \$e\$ includes observation error as well as error due to the fact that the simulator \$η(x)\$ may be systematically different from reality \$ζ(x)\$ for input condition \$x\$. Our goal is to use the observed data \$y\$ to make inference about the spatial input parameters \$x\$ ‒ predict \$x\$ and characterize the uncertainty in the prediction for \$x\$. rv: