prior agrees with Jeffreys’ rule. For other classes the compact subset normalization argu- ment may introduce a difference. A possible generalization of our treatment is to unbinned likelihoods. Since our Method-1 and 2 numerical algorithms make no assumptions about the likelihood function, they can be generalized to the unbinned case. However, the Method-1 algorithm does not implement the compact subset normalization and is therefore only ap- plicable to cases where this procedure makes no difference. Method 2 requires no compact subset normalization but makes an extra assumption about the conditional nuisance prior. For problems that involve a single continuous parameter or that can be reduced to this case by a Method-2-type integration, Ref. [12] proposes a numerical algorithm that is based directly on Eq. (14) and is therefore very general. However we found that this algorithm presents some difficulties for the complicated likelihood functions used in high energy physics. One difficulty is the round-off error in the product of large numbers of probability densities. Another difficulty is the assessment of the convergence of the integrals in the formula, and of the convergence of the finite-sample priors to the reference prior. Another possible generalization is to problems with more than one parameter of interest, as for example in the measurement of the individual single top production cross sections in the s and t channels. For this situation the reference prior algorithm requires one to sort the parameters of interest by order of importance [12], and the results may depend on this ordering. A possible interpretation of this dependence is that it is a measure of the robustness of the result to the choice of prior. This is an area that requires more study. We have developed a general-use software package that implements the methods described in this paper and have released it to the Physics Statistics Code Repository (phystat.org). Finally, we note that the main ideas underlying the construction of reference priors, namely generality, reparametrization invariance, coherence, and sampling consistency, have motivated the development of methods for summarizing reference posteriors via point esti- mates, intervals, and hypothesis tests. This subfield of objective Bayesianism is known as reference analysis [12, 13]. We thank the D0 Collaboration for its support of this work and for granting us permission to use a channel of its single top data. We also thank the members of the CMS Statistics 25 Acknowledgments