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arXiv · 2312.03911

Improving Gradient-guided Nested Sampling for Posterior Inference

Pablo Lemos·Nikolay Malkin·Will Handley·Yoshua Bengio·Yashar Hezaveh·Laurence Perreault-Levasseur
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Abstract

We present a performant, general-purpose gradient-guided nested sampling algorithm, ${\tt GGNS}$, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows ${\tt GGNS}$ to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.

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Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur. 2023-12-06. Improving Gradient-guided Nested Sampling for Posterior Inference. https://arxiv.org/abs/2312.03911

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