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arXiv 1602.01746astro-ph.CO
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优化参数约束:Fisher矩阵预测的新工具

Optimizing parameter constraints: a new tool for Fisher matrix forecasts

L. Amendola, E. Sellentin

摘要:

在贝叶斯上下文中,理论参数是相关的随机变量。然后,可以通过更精确地测量一个参数或通过更精确地测量其他参数来改善对该参数的约束。特别是在许多参数的情况下,需要一个漫长的猜测过程,以确定最有效的方式来改善一个参数的约束。在这篇简短的文章中,我们强调了一个非常简单的分析表达式,它取代了猜测,有助于更深入地理解相互依赖的参数的优化。

英文摘要:

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 article, we highlight an extremely simple analytical expression that replaces the guesswork and that facilitates a deeper understanding of optimization with interdependent parameters.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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