Bayesian analysis of multiple direct detection experiments
  Bayesian methods offer a coherent and efficient framework for implementing
uncertainties into induction problems. In this article, we review how this
approach applies to the analysis of dark matter direct detection experiments.
In particular we discuss the exclusion limit of XENON100 and the debated hints
of detection under the hypothesis of a WIMP signal. Within parameter inference,
marginalizing consistently over uncertainties to extract robust posterior
probability distributions, we find that the claimed tension between XENON100
and the other experiments can be partially alleviated in isospin violating
scenario, while elastic scattering model appears to be compatible with the
frequentist statistical approach. We then move to model comparison, for which
Bayesian methods are particularly well suited. Firstly, we investigate the
annual modulation seen in CoGeNT data, finding that there is weak evidence for
a modulation. Modulation models due to other physics compare unfavorably with
the WIMP models, paying the price for their excessive complexity. Secondly, we
confront several coherent scattering models to determine the current best
physical scenario compatible with the experimental hints. We find that
exothermic and inelastic dark matter are moderatly disfavored against the
elastic scenario, while the isospin violating model has a similar evidence.
Lastly the Bayes' factor gives inconclusive evidence for an incompatibility
between the data sets of XENON100 and the hints of detection. The same question
assessed with goodness of fit would indicate a 2 sigma discrepancy. This
suggests that more data are therefore needed to settle this question.
