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We develop an efficient, non-parametric Bayesian method for reconstructingthe time evolution of the dark energy equation of state w(z) from observationaldata. Of particular importance is the choice of prior, which must be chosencarefully to minimise variance and bias in the reconstruction. Using aprincipal component analysis, we show how a correlated prior can be used tocreate a smooth reconstruction and also avoid bias in the mean behaviour ofw(z). We test our method using Wiener reconstructions based on Fisher matrixprojections, and also against more realistic MCMC analyses of simulated datasets for Planck and a future space-based dark energy mission. While theaccuracy of our reconstruction depends on the smoothness of the assumed w(z),the relative error for typical dark energy models is <10% out to redshiftz=1.5.

Fables of reconstruction: controlling bias in the dark energy equation of state