论文标题
与混合物密度网络的无似然推理
Likelihood-free Inference with Mixture Density Network
论文作者
论文摘要
在这项工作中，我们建议使用混合密度网络（MDN）估算宇宙学参数。我们通过使用IA Supernovae类型和宇宙微波背景的功率光谱来约束$λ$ CDM和$ W $ CDM型号的参数来测试MDN方法。我们发现，MDN方法可以达到与马尔可夫链蒙特卡洛方法相同的准确性，而$ \ Mathcal {o}（10^{ - 2}σ）$略有差。此外，MDN方法可以通过$ \ Mathcal {O}（10^3）$正向模拟样本提供准确的参数估计，这对于复杂和资源消耗的宇宙学模型很有用。该方法可以处理一个数据集或多个数据集以达到参数的联合约束，对于更广泛的科学领域中复杂模型的任何参数估计都可以扩展。因此，MDN为参数的无似然推断提供了另一种方法。
In this work, we propose using the mixture density network (MDN) to estimate cosmological parameters. We test the MDN method by constraining parameters of the $Λ$CDM and $w$CDM models using Type Ia supernovae and the power spectra of the cosmic microwave background. We find that the MDN method can achieve the same level of accuracy as the Markov Chain Monte Carlo method, with a slight difference of $\mathcal{O}(10^{-2}σ)$. Furthermore, the MDN method can provide accurate parameter estimates with $\mathcal{O}(10^3)$ forward simulation samples, which are useful for complex and resource-consuming cosmological models. This method can process either one data set or multiple data sets to achieve joint constraints on parameters, extendable for any parameter estimation of complicated models in a wider scientific field. Thus, the MDN provides an alternative way for likelihood-free inference of parameters.
