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Bayesian methods offer a coherent and efficient framework for implementinguncertainties into induction problems. In this article, we review how thisapproach applies to the analysis of dark matter direct detection experiments.In particular we discuss the exclusion limit of XENON100 and the debated hintsof detection under the hypothesis of a WIMP signal. Within parameter inference,marginalizing consistently over uncertainties to extract robust posteriorprobability distributions, we find that the claimed tension between XENON100and the other experiments can be partially alleviated in isospin violatingscenario, while elastic scattering model appears to be compatible with thefrequentist statistical approach. We then move to model comparison, for whichBayesian methods are particularly well suited. Firstly, we investigate theannual modulation seen in CoGeNT data, finding that there is weak evidence fora modulation. Modulation models due to other physics compare unfavorably withthe WIMP models, paying the price for their excessive complexity. Secondly, weconfront several coherent scattering models to determine the current bestphysical scenario compatible with the experimental hints. We find thatexothermic and inelastic dark matter are moderatly disfavored against theelastic scenario, while the isospin violating model has a similar evidence.Lastly the Bayes' factor gives inconclusive evidence for an incompatibilitybetween the data sets of XENON100 and the hints of detection. The same questionassessed with goodness of fit would indicate a 2 sigma discrepancy. Thissuggests that more data are therefore needed to settle this question.

Bayesian analysis of multiple direct detection experiments