CG models can be constructed in the "top-down" or the "bottom-up" ways. The "top-down" way involves directly parameterizing the model using macroscopic experimental data, such as the elastic network model. The "bottom-up" approach primarily involves rigorously developing CG models based on statistical theories starting from AA models. It allows the mapping of statistical AA results onto CG models, aiming to ensure that CG models reproduce the statistical outcomes of AA models. Such methods reported in the literature include the iterative Boltzmann inversion, force matching, relative entropy minimization, and the generalized Yvon-Born-Green equations and so on. Although the bottom-up development of CG methods is physically rigorous, a prerequisite for using this method is the availability of an AA trajectory. Additionally, the applicability of this method is greatly influenced by the dynamics sampling of target systems.