Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up

arXiv:2405.03293v1 Announce Type: cross
Abstract: In this paper, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial role in cosmological parameter estimation, providing a robust framework for extracting theoretical insights from observational data. However, its computational demands can be substantial, primarily due to the need for numerous likelihood function evaluations. Our proposed method utilizes the power of deep learning, employing feedforward neural networks to approximate the …

abstract algorithms arxiv astro-ph.co astro-ph.im bayesian bayesian inference computational cs.lg cs.ne data deep learning framework however inference insights novel paper process robust role sampling speed stat.ml type