We investigate cosmological parameter inference and model selection from a Bayesian perspective. Type Ia supernova data from the Dark Energy Survey (DES-SN5YR) are used to test the \(Λ\)CDM, \(w\)CDM, and CPL cosmological models. Posterior inference is performed via Hamiltonian Monte Carlo using the No-U-Turn Sampler (NUTS) implemented in NumPyro and analyzed with ArviZ in Python. Bayesian model comparison is conducted through Bayes factors computed using the \texttt{bridgesampling} library in R. The results indicate that all three models demonstrate similar predictive performance, but \(w\)CDM shows stronger evidence relative to \(Λ\)CDM and CPL. We conclude that, under the assumptions and data used in this study, \(w\)CDM provides a better description of cosmological expansion.

翻译：我们从贝叶斯视角研究了宇宙学参数推断与模型选择。利用暗能量巡天（DES-SN5YR）的Ia型超新星数据，对ΛCDM、wCDM和CPL宇宙学模型进行了检验。后验推断通过哈密顿蒙特卡洛方法实现，使用NumPyro中实现的No-U-Turn采样器（NUTS），并借助Python的ArviZ进行分析。贝叶斯模型比较通过R语言中bridgesampling库计算的贝叶斯因子进行。结果表明，三种模型均展现出相似的预测性能，但wCDM相对于ΛCDM和CPL具有更强的证据支持。我们得出结论：在本研究采用的假设与数据条件下，wCDM能更好地描述宇宙膨胀现象。