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: