无证据贝叶斯模型选择：应用于暗能量状态方程
Bayesian model selection without evidences: application to the dark energy equation-of-state
摘要：
提出了一种贝叶斯模型选择的方法，而不需要明确地计算证据，通过使用组合似然并引入整数模型选择参数$n$，使得贝叶斯因子或更一般地后验比值比可以直接从$n$的后验中读出。如果所考虑的模型的总数是先验指定的，则模型的全联合参数空间$(θ, n)$具有固定的维度，并且可以使用标准的马尔可夫链蒙特卡罗（MCMC）或嵌套采样方法来探索，而不需要可逆跳MCMC技术。$n$上的后验通过直接边缘化获得。我们证明了我们的方法的有效性，应用到几个玩具模型。然后，我们将其应用到约束的暗能量状态方程使用自由形式的重建技术。我们发现，$Λ$ CDM是显着优于所有的扩展，包括简单的$w(z){=}{\rm constant}$模型。
英文摘要：
A method is presented for Bayesian model selection without explicitly computing evidences, by using a combined likelihood and introducing an integer model selection parameter $n$ so that Bayes factors, or more generally posterior odds ratios, may be read off directly from the posterior of $n$. If the total number of models under consideration is specified a priori, the full joint parameter space $(θ, n)$ of the models is of fixed dimensionality and can be explored using standard Markov chain Monte Carlo (MCMC) or nested sampling methods, without the need for reversible jump MCMC techniques. The posterior on $n$ is then obtained by straightforward marginalisation. We demonstrate the efficacy of our approach by application to several toy models. We then apply it to constraining the dark energy equation-of-state using a free-form reconstruction technique. We show that $Λ$CDM is significantly favoured over all extensions, including the simple $w(z){=}{\rm constant}$ model.
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。
