The Gaussian linear model provides a unique way to obtain the posterior probability distribution as well as the Bayesian evidence analytically. Considering the expan- sion rate data, the Gaussian linear model can be applied for ΛCDM, wCDM and a non-flat ΛCDM. In this paper, we sim- ulate 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 range σ ∈ (0.5, 10)% and two different sampling rates have been considered. Our results indicate that considering σ = 0.5% uncertainty, it is possible to discriminate 2% deviation in equation of state from w = −1. On the other hand, we investigate how pre- cision of the expansion rate data affects discriminating the ΛCDM from a non-flat ΛCDM model. Finally, we perform a parameters inference in both the MCMC and Gaussian lin- ear model, using current available expansion rate data and compare the results.
