2008
DOI: 10.1088/1126-6708/2008/12/024
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The impact of priors and observables on parameter inferences in the constrained MSSM

Abstract: We use a newly released version of the SuperBayeS code to analyze the impact of the choice of priors and the influence of various constraints on the statistical conclusions for the preferred values of the parameters of the Constrained MSSM. We assess the effect in a Bayesian framework and compare it with an alternative likelihood-based measure of a profile likelihood. We employ a new scanning algorithm (MultiNest) which increases the computational efficiency by a factor ∼ 200 with respect to previously used te… Show more

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Cited by 190 publications
(297 citation statements)
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References 69 publications
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“…The resulting chains are analysed with an adapted version of the accompanying package GetDist, supplemented with matlab scripts from the package SuperBayeS [112,113]. One-or two-dimensional marginal posterior pdfs are obtained from the chains by dividing the relevant parameter subspace into bins and counting the number of samples per bin.…”
Section: Discussionmentioning
confidence: 99%
“…The resulting chains are analysed with an adapted version of the accompanying package GetDist, supplemented with matlab scripts from the package SuperBayeS [112,113]. One-or two-dimensional marginal posterior pdfs are obtained from the chains by dividing the relevant parameter subspace into bins and counting the number of samples per bin.…”
Section: Discussionmentioning
confidence: 99%
“…This is hardly the only problem faced by the theory (e.g., Kroupa et al 2010;Kuzio de Naray & Spekkens 2011;Bovill & Ricotti 2011), so the persistent failure to detect weakly interacting massive particles (WIMPs) is worrisome. The XENON100 Collaboration (2011) has excluded much of the mass-cross-section parameter space where WIMPs were expected to reside (Trotta et al 2008). Given the persistence of dynamical puzzles (e.g., McGaugh & de Blok 1998a;Sellwood & Kosowsky 2001), one might worry that non-baryonic dark matter does not exist after all.…”
Section: General Constraintsmentioning
confidence: 99%
“…This is an updated and improved version of the publicly available SuperBayeS scanning package [43,44]. This Bayesian algorithm, originally designed to compute a model's likelihood and to accurately map out the posterior distribution, can also reliably evaluate the profile likelihood, given appropriate settings [38].…”
Section: Jhep09(2016)175mentioning
confidence: 99%