Gaussian discriminators between $\Lambda$CDM and wCDM cosmologies using
  expansion data
  The Gaussian linear model provides a unique way to obtain the posterior
probability distribution as well as the Bayesian evidence analytically.
Considering the expansion rate data, the Gaussian linear model can be applied
for $\Lambda$CDM, wCDM, and a non-flat $\Lambda$CDM. In this paper, we simulate
the expansion data with various precision and obtain the Bayesian evidence,
then it has been used to discriminate the models. The data uncertainty is in
the range $\sigma\in(0.5,10)\%$ and two different sampling rates have been
considered. Our results indicate that it is possible to discriminate $w=-1.02$
(or $w=-0.98$) model from the $\Lambda$CDM $(w=-1)$ with $\sigma=0.5\%$
uncertainty in expansion rate data. Finally, we perform a parameters inference
in both the MCMC and Gaussian linear model, using currently available expansion
rate data, and compare the results.
