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We propose a new Stein self-repulsive dynamics for obtaining diversified samples from intractable un-normalized distributions.
Function space particle optimization for bayesian neural networks
Ziyu Wang, Tongzheng Ren, Jun Zhu, and Bo Zhang · 1902
Earlier work this paper cites.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R. Thompson · 1933
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Brownian dynamics as smart monte carlo simulation
Peter J Rossky, JD Doll, and HL Friedman · 1978
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Mushroom records drawn from the audubon society field guide to north american mushrooms
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Introduction to strong mixing conditions
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