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 |
| Zusammenfassung: | 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 of guesswork is then needed to determine the most efficient way to improve one parameter's constraints. In this short paper, we highlight an extremely simple analytical expression that replaces the guesswork and that facilitates a deeper understanding of optimization with interdependent parameters. |
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| Beschreibung: | Gesehen am 11.10.2017 |
| Beschreibung: | Online Resource |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stw072 |