  Field experiments are often difficult and expensive to make. To bypass these
issues, industrial companies have developed computational codes. These codes
intend to be representative of the physical system, but come with a certain
amount of problems. The code intends to be as close as possible to the physical
system. It turns out that, despite continuous code development, the difference
between the code outputs and experiments can remain significant. Two kinds of
uncertainties are observed. The first one comes from the difference between the
physical phenomenon and the values recorded experimentally. The second concerns
the gap between the code and the physical system. To reduce this difference,
often named model bias, discrepancy, or model error, computer codes are
generally complexified in order to make them more realistic. These improvements
lead to time consuming codes. Moreover, a code often depends on parameters to
be set by the user to make the code as close as possible to field data. This
estimation task is called calibration. This paper proposes a review of Bayesian
calibration methods and is based on an application case which makes it possible
to discuss the various methodological choices and to illustrate their
divergences. This example is based on a code used to predict the power of a
photovoltaic plant.
