Intrepid MCMC: Metropolis-Hastings with Exploration
Promit Chakroborty, Michael D. Shields

TL;DR
Intrepid MCMC introduces a coordinate transformation to enhance the mode exploration and convergence of Metropolis-Hastings algorithms, especially for complex, multi-modal distributions, demonstrated through various examples and a Bayesian inference application.
Contribution
The paper proposes a novel MH scheme called Intrepid MCMC that improves mode-finding and convergence by using a simple coordinate transformation, maintaining simplicity of the MH framework.
Findings
Significant improvement over vanilla MH in sampling complex distributions
Enhanced mixing behavior and convergence rates demonstrated
Effective in high-dimensional Bayesian inference problems
Abstract
In engineering examples, one often encounters the need to sample from unnormalized distributions with complex shapes that may also be implicitly defined through a physical or numerical simulation model, making it computationally expensive to evaluate the associated density function. For such cases, MCMC has proven to be an invaluable tool. Random-walk Metropolis Methods (also known as Metropolis-Hastings (MH)), in particular, are highly popular for their simplicity, flexibility, and ease of implementation. However, most MH algorithms suffer from significant limitations when attempting to sample from distributions with multiple modes (particularly disconnected ones). In this paper, we present Intrepid MCMC - a novel MH scheme that utilizes a simple coordinate transformation to significantly improve the mode-finding ability and convergence rate to the target distribution of random-walk…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Parallel Computing and Optimization Techniques
