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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 …
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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 …
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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 …
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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 …
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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 …
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