# Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics

## TL;DR

Gaussbock is a parallel iterative algorithm that leverages Bayesian nonparametrics and importance sampling to significantly accelerate cosmological parameter estimation, especially suited for high-dimensional problems and large-scale computing resources.

## Contribution

It introduces a novel, fast, and parallelizable method for cosmological parameter estimation that reduces computational time and handles complex posterior distributions effectively.

## Key findings

- Achieves an order-of-magnitude speed-up over traditional methods
- Performs well on low-dimensional complex posteriors
- Faces challenges with high-dimensional distributions

## Abstract

We present and apply Gaussbock, a new embarrassingly parallel iterative algorithm for cosmological parameter estimation designed for an era of cheap parallel computing resources. Gaussbock uses Bayesian nonparametrics and truncated importance sampling to accurately draw samples from posterior distributions with an orders-of-magnitude speed-up in wall time over alternative methods. Contemporary problems in this area often suffer from both increased computational costs due to high-dimensional parameter spaces and consequent excessive time requirements, as well as the need for fine tuning of proposal distributions or sampling parameters. Gaussbock is designed specifically with these issues in mind. We explore and validate the performance and convergence of the algorithm on a fast approximation to the Dark Energy Survey Year 1 (DES Y1) posterior, finding reasonable scaling behavior with the number of parameters. We then test on the full DES Y1 posterior using large-scale supercomputing facilities, and recover reasonable agreement with previous chains, although the algorithm can underestimate the tails of poorly-constrained parameters. Additionally, we discuss and demonstrate how Gaussbock recovers complex posterior shapes very well at lower dimensions, but faces challenges to perform well on such distributions in higher dimensions. In addition, we provide the community with a user-friendly software tool for accelerated cosmological parameter estimation based on the methodology described in this paper.