Model selection and parameter estimation using the iterative smoothing method

Understand

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.