论文标题
使用迭代平滑方法的模型选择和参数估计
Model selection and parameter estimation using the iterative smoothing method
论文作者
论文摘要
我们在一组类似模拟万神殿的IA Supernova数据集上，从非参数迭代平滑方法中计算了可能性的分布。我们使用此可能性分布来测试典型的暗能量模型是否与数据一致并执行参数估计。在这种方法中，确定模型和数据的一致性无需与其他替代模型进行比较。我们研究了未来的WFIRST样数据，我们研究了II型错误，并显示了我们如何使用这种非参数方法来确定不同的暗能量模型。
We compute the distribution of likelihoods from the non-parametric iterative smoothing method over a set of mock Pantheon-like type Ia supernova datasets. We use this likelihood distribution to test whether typical dark energy models are consistent with the data and to perform parameter estimation. In this approach, the consistency of a model and the data is determined without the need for comparison with another alternative model. Simulating future WFIRST-like data, we study type II errors and show how confidently we can distinguish different dark energy models using this non-parametric approach.
