Bayesian evidence and model selection approach for time-dependent dark energy

We use parameterized post-Friedmann (PPF) description for dark energy and apply ellipsoidal nested sampling to perform the Bayesian model selection method on different time-dependent models using a combination of $Planck$ data based distance measurements, namely baryon acoustic oscillations supernovae luminosity distance. Models with two three free parameters described in terms linear scale factor $a$, or scaled units e-folding $\ln a$ are considered. Our results show that parameterizing provides better constraints than polynomial expressions. In general, free-parameter adequate describe dynamics compared their generalizations. According evidence, determining strength support cosmological constant $\Lambda$ over remains inconclusive. Furthermore, considering $R$ statistic as tension metric shows one gives rise between measurements sets. The preference logarithmic equation state is inconclusive, $\rm \Lambda$CDM oscillating moderate.