# Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks

arXiv: https://arxiv.org/abs/2112.11865 · HTML: https://mlsanity.com/paper/2112.11865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches](https://mlsanity.com/paper/1803.04386/) (2018)
- [Bayesian Neural Networks: Essentials](https://mlsanity.com/paper/2106.13594/) (2021)

## Built on by (most cited first, 1 of 1)

- [Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networks](https://mlsanity.com/paper/2301.03991/) (2023)

## Abstract (arXiv)

In this paper, we use The Quijote simulations in order to extract the cosmological parameters through Bayesian Neural Networks. This kind of model has a remarkable ability to estimate the associated uncertainty, which is one of the ultimate goals in the precision cosmology era. We demonstrate the advantages of BNNs for extracting more complex output distributions and non-Gaussianities information from the simulations.