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2
Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains
One of the main technical challenges associated with likelihood-based inference for big data is the fact that likelihood calculation is …
J Bierkens
,
A Bouchard-Côté
,
A Doucet
,
others
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Multilevel Monte Carlo for Reliability Theory
As the size of engineered systems grows, problems in reliability theory can become computationally challenging, often due to the …
Louis J M Aslett
,
Tigran Nagapetyan
,
Sebastian J Vollmer
Cite
An iterative technique for bounding derivatives of solutions of Stein equations
Project Euclid - mathematics and statistics online
Christian Döbler
,
Robert E Gaunt
,
Sebastian J Vollmer
Cite
An iterative technique for bounding derivatives of solutions of Stein equations
Christian Döbler
,
Robert E Gaunt
,
Sebastian J Vollmer
,
others
Cite
Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server
This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation …
Leonard Hasenclever
,
Stefan Webb
,
Thibaut Lienart
,
Sebastian Vollmer
,
Balaji Lakshminarayanan
,
Charles Blundell
,
Yee Whye Teh
Cite
Note on A. Barbour's paper on Stein's method for diffusion approximations
In [2] foundations for diffusion approximation via Stein’s method are laid. This paper has been cited more than 130 times and is …
Mikołaj J Kasprzak
,
Andrew B Duncan
,
Sebastian J Vollmer
,
others
Cite
Relativistic monte carlo
Abstract Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a …
X Lu
,
V Perrone
,
L Hasenclever
,
others
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The true cost of stochastic gradient Langevin dynamics
The problem of posterior inference is central to Bayesian statistics and a wealth of Markov Chain Monte Carlo (MCMC) methods have been …
T Nagapetyan
,
A B Duncan
,
L Hasenclever
,
others
Cite
Consistency and fluctuations for stochastic gradient Langevin dynamics
Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally expensive. Both the calculation of …
Yee Whye Teh
,
Alexandre H Thiery
,
Sebastian J Vollmer
Cite
Consistency and fluctuations for stochastic gradient Langevin dynamics
Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally expensive. Both the calculation of …
Yee Whye Teh
,
Alexandre H Thiery
,
Sebastian J Vollmer
Cite
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