Search Results for author:
Found 7 papers, 1 papers with code
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
We exhibit similar bounds using both approximate and stochastic gradients, and our method's computational cost is shown to scale logarithmically with the size of the dataset.
A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators
However, a fixed non-degenerate prior leads to a divergent posterior mean when the observation noise becomes small, if the data induces a perturbation in the eigenspace of zero eigenvalues of the inversion operator.
Unbiased Estimation using Underdamped Langevin Dynamics
In this work we consider the unbiased estimation of expectations w. r. t.~probability measures that have non-negative Lebesgue density, and which are known point-wise up-to a normalizing constant.
Multilevel Bayesian Deep Neural Networks
In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which were proposed by Sell et al. [39].
Consistency analysis of bilevel data-driven learning in inverse problems
One fundamental problem when solving inverse problems is how to find regularization parameters.
Unbiased inference for discretely observed hidden Markov model diffusions
The resulting estimator leads to inference without a bias from the time-discretisation as the number of Markov chain iterations increases.
Bayesian Inference
Methodology
Probability
Computation
65C05 (primary), 60H35, 65C35, 65C40 (secondary)
Analysis of Hierarchical Ensemble Kalman Inversion
We discuss properties of hierarchical Bayesian inversion through the ensemble Kalman filter (EnKF).
Numerical Analysis
