We apply the Bayesian model selection method (based on the Bayes factor) to optimize $\sqrt{s_{NN}}$-dependence in the phenomenological parameters of the $(3+1)$-dimensional hybrid framework for describing relativistic heavy-ion collisions within the beam energy scan program at the Relativistic Heavy Ion Collider. The effects of various experimental measurements on the posterior distribution are investigated. We also make model predictions for longitudinal flow decorrelation, rapidity-dependent anisotropic flow, and identified particle $v_0\left(p_{\mathrm{T}}\right)$ in $\mathrm{Au}+\mathrm{Au}$ collisions, as well as anisotropic flow coefficients in small systems. Systematic uncertainties in the model predictions are estimated using the variance of the simulation results with a few parameter sets sampled from the posterior distributions.

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• Collective flow
• Hydrodynamic models
• Quark-gluon plasma
• Relativistic heavy-ion collisions
• Bayesian methods
• Machine learning