2012
DOI: 10.1088/1475-7516/2012/02/048
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Fables of reconstruction: controlling bias in the dark energy equation of state

Abstract: We develop an efficient, non-parametric Bayesian method for reconstructing the time evolution of the dark energy equation of state w(z) from observational data. Of particular importance is the choice of prior, which must be chosen carefully to minimise variance and bias in the reconstruction. Using a principal component analysis, we show how a correlated prior can be used to create a smooth reconstruction and also avoid bias in the mean behaviour of w(z). We test our method using Wiener reconstructions based o… Show more

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Cited by 100 publications
(137 citation statements)
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References 67 publications
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“…Any representation of a function with a finite number of parameters is necessarily inaccurate and, in the absence of additional priors, the outcome of the fit would directly depend on N . As shown by Crittenden et al [27], it is possible to eliminate the dependence on N and explicitly control the reconstruction bias by adding a prior that correlates the nearby bins.…”
Section: The Reconstruction Methodsmentioning
confidence: 99%
“…Any representation of a function with a finite number of parameters is necessarily inaccurate and, in the absence of additional priors, the outcome of the fit would directly depend on N . As shown by Crittenden et al [27], it is possible to eliminate the dependence on N and explicitly control the reconstruction bias by adding a prior that correlates the nearby bins.…”
Section: The Reconstruction Methodsmentioning
confidence: 99%
“…Here we follow Crittenden et al (2009), but we modify their method to use scale factor rather than redshift as the independent variable (see also Crittenden et al 2012), adopting a correlation function…”
Section: Constraints On W(z) In the General Modelmentioning
confidence: 99%
“…To address these issues, we recently introduced a new reconstruction method which imposes an explicit prior directly on the space of w(z) functions, which is combined with observations in a straightforward Bayesian framework and is simple to implement [4,6]. (The Gaussian Process method [7][8][9] is close in spirit to our approach, but the methods and interpretation of the results differ significantly.)…”
mentioning
confidence: 99%
“…In [6] we have considered other choices of correlation functions and found that changing the shape of the correlation function does not significantly impact the reconstruction results.…”
mentioning
confidence: 99%