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2
Exploration of the (non-)asymptotic bias and variance of stochastic gradient langevin dynamics
Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally infeasible. The recently proposed …
Sebastian J Vollmer
,
Konstantinos C Zygalakis
,
Yee Whye Teh
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An example journal article
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Sebastian Vollmer
,
Robert Ford
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Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors
The computational complexity of Markov chain Monte Carlo (MCMC) methods for the exploration of complex probability measures is a …
Sebastian J Vollmer
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Bayesian inference with big data: a snapshot from a workshop
Over the last half century, and particularly since the advent of Markov Chain Monte Carlo methods, Bayesian inference has enjoyed …
M Welling
,
Y W Teh
,
C Andrieu
,
J Kominiarczuk
,
others
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Spectral gaps for a Metropolis--Hastings algorithm in infinite dimensions
We study the problem of sampling high and infinite dimensional target measures arising in applications such as conditioned diffusions …
M Hairer
,
A M Stuart
,
S J Vollmer
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Posterior consistency for Bayesian inverse problems through stability and regression results
In the Bayesian approach, the a priori knowledge about the input of a mathematical model is described via a probability measure. The …
S J Vollmer
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