GG-PA composes diffusion priors with physical context via a derived Gibbs sampler that is asymptotically exact as diffusion time approaches zero and exact at finite times for quadratic interactions.
Equation of state calculations by fast computing machines.The Journal of Chemical Physics, 21(6):1087–1092
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Under a block-wise contraction condition, spectral gaps of random-scan and deterministic-scan component-wise Markov chains are simultaneously positive or zero and differ by at most polynomial factors in the number of blocks.
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Composing diffusion priors with explicit physical context via generative Gibbs sampling
GG-PA composes diffusion priors with physical context via a derived Gibbs sampler that is asymptotically exact as diffusion time approaches zero and exact at finite times for quadratic interactions.
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Solidarity of Spectral Gaps for Component-Wise Markov Chains
Under a block-wise contraction condition, spectral gaps of random-scan and deterministic-scan component-wise Markov chains are simultaneously positive or zero and differ by at most polynomial factors in the number of blocks.