Scalable Monte Carlo Methods for large-scale infererence
Lead Research Organisation:
University of Oxford
Department Name: Statistics
Abstract
We are in the era of Big Data and big models. In this context, standard Monte Carlo methods such as Markov Chain Monte Carlo methods do not scale, ie the computational complexity is far too high. The aim of the DPhil is to develop, analyse and apply new scalable Monte Carlo methods, which are particularly suitable for an implementation on large-scale distributed systems.
The project will fall within the EPSRC statistics and applied probability research area
The project will fall within the EPSRC statistics and applied probability research area
Organisations
People |
ORCID iD |
| Arnaud Doucet (Primary Supervisor) | |
| Sebastian Schmon (Student) |
Publications
Schmon Sebastian M
(2019)
Bernoulli Race Particle Filters
in arXiv e-prints
Schmon Sebastian M.
(2018)
Large Sample Asymptotics of the Pseudo-Marginal Method
in arXiv e-prints
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509711/1 | 30/09/2016 | 29/09/2021 | |||
| 1656790 | Studentship | EP/N509711/1 | 30/09/2015 | 31/03/2019 | Sebastian Schmon |