Improving Gradient-guided Nested Sampling for Posterior Inference

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

Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

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Keywords: nested sampling, generative flow networks, bayesian inference

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TL;DR: We introduce gradient-guided nested sampling for efficient exploration of complex distributions. Additionally, we enhance mode discovery and partition function estimation by combining with generative flow networks

Abstract:

We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows 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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Submission Number: 6559