Optimizing parameter constraints: a new tool for Fisher matrix forecasts
In a Bayesian context, theoretical parameters are correlated random variables. Then, the constraints on one parameter can be improved by either measuring this parameter more precisely - or by measuring the other parameters more precisely. Especially in the case of many parameters, a lengthy process...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
04 February 2016
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| In: |
Monthly notices of the Royal Astronomical Society
Year: 2016, Jahrgang: 457, Heft: 2, Pages: 1490-1495 |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stw072 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1093/mnras/stw072
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| Verfasserangaben: | Luca Amendola and Elena Sellentin |
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Optimizing parameter constraints: a new tool for Fisher matrix forecasts
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