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 likelihood function dynamically during the
Bayesian inference process. Unlike traditional approaches, our method trains
neural networks on-the-fly using the current set of live points as training
data, without the need for pre-training. This flexibility enables adaptation to
various theoretical models and datasets. We perform simple hyperparameter
optimization using genetic algorithms to suggest initial neural network
architectures for learning each likelihood function. Once sufficient accuracy
is achieved, the neural network replaces the original likelihood function. The
implementation integrates with nested sampling algorithms and has been
thoroughly evaluated using both simple cosmological dark energy models and
diverse observational datasets. Additionally, we explore the potential of
genetic algorithms for generating initial live points within nested sampling
inference, opening up new avenues for enhancing the efficiency and
effectiveness of Bayesian inference methods.

本文提出了一种新颖的方法来加速贝叶斯推断过程，特别关注嵌套取样算法。