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Bayesian inference typically requires the computation of an approximation to the posterior distribution.
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Estimation of Non-Normalized Statistical Models by Score Matching
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Optimal transport: old and new
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Quantitative bounds for Markov chain convergence: Wasserstein and total variation distances
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Scalable training of mixture models via coresets
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
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Convergence to equilibrium in Wasserstein distance for Fokker–Planck equations
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PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
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Huggins, J. H · 2017
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Simple, scalable and accurate posterior interval estimation
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Ogden, H. E · 2017
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Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
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