Improving Gradient-guided Nested Sampling for Posterior Inference
Pablo Lemos Nikolay Malkin Will Handley Yoshua Bengio Yashar Hezaveh Laurence Perreault-Levasseur
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.