# Bayesian Simulation-based Inference for Cosmological Initial Conditions

arXiv: https://arxiv.org/abs/2310.19910 · HTML: https://mlsanity.com/paper/2310.19910/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Built on by (most cited first, 2 of 2)

- [TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information](https://mlsanity.com/paper/2605.07720/) (2026)
- [Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions](https://mlsanity.com/paper/2606.10023/) (2026)

## Abstract (arXiv)

Reconstructing astrophysical and cosmological fields from observations is challenging. It requires accounting for non-linear transformations, mixing of spatial structure, and noise. In contrast, forward simulators that map fields to observations are readily available for many applications. We present a versatile Bayesian field reconstruction algorithm rooted in simulation-based inference and enhanced by autoregressive modeling. The proposed technique is applicable to generic (non-differentiable) forward simulators and allows sampling from the posterior for the underlying field. We show first promising results on a proof-of-concept application: the recovery of cosmological initial conditions from late-time density fields.