BibTeX

@article{2501.03921v1,
Author        = {Nicolas Mediato-Diaz and Will Handley},
Title         = {Cosmological Parameter Estimation with Sequential Linear
  Simulation-based Inference},
Eprint        = {2501.03921v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {We develop the framework of Linear Simulation-based Inference (LSBI), an
application of simulation-based inference where the likelihood is approximated
by a Gaussian linear function of its parameters. We obtain analytical
expressions for the posterior distributions of hyper-parameters of the linear
likelihood in terms of samples drawn from a simulator, for both uniform and
conjugate priors. This method is applied sequentially to several toy-models and
tested on emulated datasets for the Cosmic Microwave Background temperature
power spectrum. We find that convergence is achieved after four or five rounds
of $\mathcal{O}(10^4)$ simulations, which is competitive with state-of-the-art
neural density estimation methods. Therefore, we demonstrate that it is
possible to obtain significant information gain and generate posteriors that
agree with the underlying parameters while maintaining explainability and
intellectual oversight.},
Year          = {2025},
Month         = {Jan},
Url           = {http://arxiv.org/abs/2501.03921v1},
File          = {2501.03921v1.pdf}
}