The paper presents a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. The proposed method utilizes the power of deep learning, employing feedforward neural networks to approximate the likelihood function dynamically during the Bayesian inference process.
The key highlights are:
The neural networks are trained 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.
Simple hyperparameter optimization using genetic algorithms is explored to suggest initial neural network architectures for learning each 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.
The authors also 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.
The authors demonstrate that their method can achieve significant speed-ups in the Bayesian inference process, ranging from 6% to 28.4% in the tested cases, without compromising the statistical reliability of the results.
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