An Accelerating Flat FLRW Model with Observation Constraints and Dynamic $\Lambda$

In this paper, we explore power law solution of FLRW universe model that is associated with a variable cosmological term $\Lambda(t)$ as a linear function of $\frac{\ddot{a}}{a}, (\frac{\dot{a}}{a})^2$ and $\rho$. The model parameters were estimated on the basis of the four data sets: The Hubble 46 data, the Union 2.1 compilation data sets comprising of distance modulus of 580 SNIa supernovae at different redshifts, the Pantheon data set which contains Apparent magnitudes of 1048 SNIa supernovae at various redshifts and finally BAO data set of volume averaged distances at 5 redshifts. We employ the conventional Bayesian methodology to analyze the observational data and also the Markov Chain Monte Carlo (MCMC) technique to derive the posterior distributions of the parameters. The best fit values of Hubble parameter $H_0$ as per the four data sets are found as $61.53^{+0.453}_{-0.456}$, $69.270^{+0.229}_{-0.228}$, $78.116^ {+0.480}_{-0.479}$, and $71.318 ^{+2.473}_{-2.283}$ respectively. Off late the present value of Hubble parameters $H_0$ were empirically given as 73 and 67.7 (km/s)/Mpc using distance ladder techniques and measurements of the cosmic microwave background. The OHD+BAO+Union and ~OHD+Pan+BAO+Union combined data sets provide the best fit Hubble parameter value $H_0$ as $67.427^{+0.197}_{-0.199}$ and $74.997^{+0.143}_{-0.145}$ respectively. The various geometrical and physical properties of the model were also investigated and were found in good agreements with observations.