  Calibration parameters in deterministic computer experiments are those
attributes that cannot be measured or available in physical experiments.
Kennedy and O'Hagan \cite{kennedy2001bayesian} suggested an approach to
estimate them by using data from physical experiments and computer simulations.
A theoretical framework is given which allows us to study the issues of
parameter identifiability and estimation. We define the $L_2$-consistency for
calibration as a justification for calibration methods. It is shown that a
simplified version of the original KO method leads to asymptotically
$L_2$-inconsistent calibration. This $L_2$-inconsistency can be remedied by
modifying the original estimation procedure. A novel calibration method, called
the $L_2$ calibration, is proposed and proven to be $L_2$-consistent and enjoys
optimal convergence rate. A numerical example and some mathematical analysis
are used to illustrate the source of the $L_2$-inconsistency problem.
