Theoretical analysis of multiproposal MCMC in the infinite proposal limit using involutive theory yields new methods and inter-method relationships.
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2026 2verdicts
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New MCMC methods employ data-driven similarity-driven proposals to improve sampling from posteriors on discrete state spaces, extending to hierarchical models without marginalizing latent variables.
citing papers explorer
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Mad Props: Parallelism in Markov Chain Monte Carlo Through the Lens of the Infinite Proposal Limit
Theoretical analysis of multiproposal MCMC in the infinite proposal limit using involutive theory yields new methods and inter-method relationships.
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Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
New MCMC methods employ data-driven similarity-driven proposals to improve sampling from posteriors on discrete state spaces, extending to hierarchical models without marginalizing latent variables.