http://arxiv.org/abs/2301.07369
A model-independent or non-parametric approach for modeling a database has been widely used in cosmology. In these scenarios, the data has been used directly to reconstruct an underlying function. In this work, we introduce a novel semi-model-independent method to do the task. The new approach not only removes some drawbacks of previous methods but also has some remarkable advantages. We combine the well-known Gaussian linear model with a neural network and introduce a procedure for the reconstruction of an arbitrary function. In the scenario, the neural network produces some arbitrary base functions which subsequently are fed to the Gaussian linear model. Given a prior distribution on the free parameters, the Gaussian linear model provides a close form for the posterior distribution as well as the Bayesian evidence. In addition, contrary to other methods, it is straightforward to compute the uncertainty.
A. Mehrabi
Thu, 19 Jan 23
33/100
Comments: Comments welcome, See this https URL for the code