Improving Gradient-Guided Nested Sampling for Posterior Inference
Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur
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.
BibTeX
@inproceedings{
lemos2024improving,
title={Improving Gradient-Guided Nested Sampling for Posterior Inference},
author={Pablo Lemos and Nikolay Malkin and Will Handley and Yoshua Bengio and Yashar Hezaveh and Laurence Perreault-Levasseur},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=K5h6VAsJaV}
}