Analysis of the H0 tension problem in the Universe with viscous dark fluid

Two inhomogeneous single-fluid models for the Universe, which are able to naturally solve the H-0 tension problem, are discussed. The analysis is based on a Bayesian Machine Learning approach that uses a generative process. The method here adopted allows for constraint of the free parameters of each model by making use of the model itself only. The observable is taken to be the Hubble parameter, obtained from the generative process. Using the full advantages of our method, the models are constrained for two redshift ranges. Namely, first this is done with mock H(z) data over z is an element of [0, 2.5], thus covering known H(z) observational data, which are most helpful to validate the fit results. Then, aiming to extend to redshift ranges to be covered by the most recent ongoing and future planned missions, the models are constrained for the range z is an element of [0, 5], too. Full validation of the results for this extended redshift range will have to wait for some years, for when higher-redshift H(z) data become available. This makes our models fully falsifiable. In addition, our second model is able to explain the BOSS reported value for H(z) at z = 2.34.