  A composite likelihood is a non-genuine likelihood function that allows to
make inference on limited aspects of a model, such as marginal or conditional
distributions. Composite likelihoods are not proper likelihoods and need
therefore calibration for their use in inference, from both a frequentist and a
Bayesian perspective. The maximizer to the composite likelihood can serve as an
estimator and its variance is assessed by means of a suitably defined sandwich
matrix. In the Bayesian setting, the composite likelihood can be adjusted by
means of magnitude and curvature methods. Magnitude methods imply raising the
likelihood to a constant, while curvature methods imply evaluating the
likelihood at a different point by translating, rescaling and rotating the
parameter vector. Some authors argue that curvature methods are more reliable
in general, but others proved that magnitude methods are sufficient to recover,
for instance, the null distribution of a test statistic. We propose a simple
calibration for the marginal posterior distribution of a scalar parameter of
interest which is invariant to monotonic and smooth transformations. This can
be enough for instance in medical statistics, where a single scalar effect
measure is often the target.
