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We investigate and compare the fundamental performance of several distributed learning methods that have been proposed recently.
On a formula for the L 2 {L}^{2} Wasserstein metric between measures on Euclidean and Hilbert spaces
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Honest adaptive confidence bands and self-similar functions
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Frequentist coverage of adaptive nonparametric Bayesian credible sets
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Mathematical foundations of infinite-dimensional statistical models
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Bayes procedures for adaptive inference in inverse problems for the white noise model
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A sharp adaptive confidence ball for self-similar functions
Nickl, R. and Szabó, B. (2016) · 2016
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Bayes and big data: The consensus Monte Carlo algorithm
Scott, S. L., Blocker, A. W., Bonassi, F. V., Chipman, H. A., George, E. I. and McCulloch, R. E. (2016) · 2016
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Distributed Gaussian processes
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A Bayesian splitotic theory for nonparametric models
Shang, Z. and Cheng, G. (2015) · 2015
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On coverage and local radial rates of credible sets
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Gaussian estimation: Sequence and wavelet models
Johnstone, I. M. (2017) · 2017
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