Diffusion posterior samplers produce biased outputs that can be expressed as an Ornstein-Uhlenbeck path expectation via a surrogate Gaussian path and Feynman-Kac representation, with STSL flattening the spatially varying bias term.
Simulated tempering: A new monte carlo scheme
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Tempering chains achieve polynomial spectral gap lower bounds of order 11-12 for multimodal Gibbs measures without explicit energy landscape structure.
Folding chains in the heteropolymer model diffuse according to D ~ t^ν with ν decreasing from 0.666 to 0.5 as coupling randomness increases.
citing papers explorer
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Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
Diffusion posterior samplers produce biased outputs that can be expressed as an Ornstein-Uhlenbeck path expectation via a surrogate Gaussian path and Feynman-Kac representation, with STSL flattening the spatially varying bias term.
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Rapid convergence of tempering chains to multimodal Gibbs measures
Tempering chains achieve polynomial spectral gap lower bounds of order 11-12 for multimodal Gibbs measures without explicit energy landscape structure.
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On the Diffusion Time Evolution of Folding Chains in the Heteropolymer Model
Folding chains in the heteropolymer model diffuse according to D ~ t^ν with ν decreasing from 0.666 to 0.5 as coupling randomness increases.