Speaker
Description
In this talk, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Our 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 the hyperparameter optimization using genetic algorithms to suggest the initial neural network architectures for learning each likelihood function. Once sufficient accuracy is achieved, the neural network replaces the original likelihood function.
