Web: http://arxiv.org/abs/2209.09349

Sept. 21, 2022, 1:11 a.m. | Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields

stat.ML updates on arXiv.org arxiv.org

Although the no-u-turn sampler (NUTS) is a widely adopted method for
performing Bayesian inference, it requires numerous posterior gradients which
can be expensive to compute in practice. Recently, there has been a significant
interest in physics-based machine learning of dynamical (or Hamiltonian)
systems and Hamiltonian neural networks (HNNs) is a noteworthy architecture.
But these types of architectures have not been applied to solve Bayesian
inference problems efficiently. We propose the use of HNNs for performing
Bayesian inference efficiently without requiring …

arxiv bayesian bayesian inference inference machine machine learning physics systems

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