The paper proves polynomial mixing time upper bounds for three data augmentation algorithms (ProbitDA, LogitDA, LassoDA) with explicit dependence on design matrix, prior, n, and d.
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Parallel Picard-map algorithms for zeroth-order Random Walk Metropolis achieve O(sqrt(d)) parallel iterations with O(sqrt(d)) processors on log-concave distributions in d dimensions.
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Fast Mixing of Data Augmentation Algorithms: Bayesian Probit, Logit, and Lasso Regression
The paper proves polynomial mixing time upper bounds for three data augmentation algorithms (ProbitDA, LogitDA, LassoDA) with explicit dependence on design matrix, prior, n, and d.
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Parallel computations for Metropolis Markov chains with Picard maps
Parallel Picard-map algorithms for zeroth-order Random Walk Metropolis achieve O(sqrt(d)) parallel iterations with O(sqrt(d)) processors on log-concave distributions in d dimensions.