模型空间的马尔可夫游走：基于超新星数据的暗能量模型贝叶斯选择
Markov Walk Exploration of Model Spaces: Bayesian Selection of Dark Energy Models with Supernovae
AI总结：
本文提出基于马尔可夫随机游走探索整个模型空间的方法，利用对数证据比进行贝叶斯模型选择，并应用于基于超新星距离-红移关系数据选择暗能量状态方程多项式。
摘要：
模型选择的核心在于良好拟合与低模型复杂度之间的权衡：仅当高复杂度模型能提供显著更好的拟合时，才应优于较简单模型被青睐。在贝叶斯框架下，这可通过考虑证据比来实现，从而在两个竞争模型间做出选择。我们通过构建马尔可夫随机游走来探索整个模型空间，从而推广了这一概念。类似于参数估计问题中的对数似然比，该过程由对数证据比控制。我们将该方法应用于基于超新星距离-红移关系数据，为暗能量状态方程函数 $w(a)$ 选择多项式。
英文摘要：
Central to model selection is a trade-off between performing a good fit and low model complexity: A model of higher complexity should only be favoured over a simpler model if it provides significantly better fits. In Bayesian terms, this can be achieved by considering the evidence ratio, enabling choices between two competing models. We generalise this concept by constructing Markovian random walks for exploring the entire model space. In analogy to the logarithmic likelihood ratio in parameter estimation problem, the process is governed by the logarithmic evidence ratio. We apply our methodology to selecting a polynomial for the dark energy equation of state function $w(a)$ on the basis of data for the supernova distance-redshift relation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
