Efficient and Fast Sampling from Arbitrary Probability Kernels using Sliced Gibbs Sampler

TL;DR

The paper introduces an Automated Sliced Gibbs (ASG) framework that enables fully automated, efficient MCMC sampling from complex, high-dimensional probability distributions without manual tuning.

Contribution

It presents a novel, tuning-free sliced Gibbs sampler that automatically detects support and adapts to irregular geometries, outperforming existing methods.

Findings

ASG achieves higher effective sample size per second across various benchmarks.

The method effectively handles non-smooth, multimodal, and heavy-tailed densities.

ASG outperforms traditional MCMC algorithms like RWMH and adaptive Gibbs in speed and mixing.

Abstract

An Automated Sliced Gibbs framework is proposed for fully automated Markov chain Monte Carlo sampling from arbitrary finite dimensional probability kernels. The method targets unnormalized, non-smooth, heavy tailed, and highly multimodal densities. A Cauchy transformation based effective support estimator is combined with slice driven Gibbs updates. This construction removes the need for user specified truncation bounds, proposal scales, step-size tuning, or conditional optimization. Unlike existing slice samplers, ASG does not require manually chosen bracket widths or geometric insight into the support. All calibration is performed automatically within each Gibbs cycle. The resulting Markov chain preserves invariance and ergodicity. Automated support detection allows efficient movement across disconnected high density regions. The sampler adapts to sharp curvature and irregular geometry without gradient information. Extensive numerical experiments evaluate performance on complex kernels, including univariate Beta mixtures, multivariate Rosenbrock and Ackley benchmarks, and non-smooth kernels derived from generalized LASSO type loss functions. Across these challenging settings, ASG consistently achieves higher effective sample size per second and faster decorrelation than Random Walk Metropolis Hastings, adaptive Gibbs variants, and some recently proposed slice based methods. The framework provides a scalable and general-purpose solution for sampling from complicated probability kernels where existing algorithms require substantial tuning or exhibit slow mixing.