Nick Whiteley
Contact
School of Mathematics
University Walk
Bristol
BS8 1TW
Office 3.5 Main Building
email: nick.whiteley[at]bristol.ac.uk
Teaching
3/M 5400 Financial Mathematics
Research
I am
interested in Monte Carlo methods, especially sequential methods and their
application to a variety of estimation, dynamical inference and optimization
problems.
Forthcoming workshop 16-19th
April 2012: Confronting
Intractability in Statistical Inference
Working Papers
- A particle
method for approximating principal eigen-functions and related quantities. With Nikolas Kantas.
2012. Submitted. [arXiv]
- Stability
properties of some particle filters. 2011. Submitted. [.pdf]
- Bayesian
Computational Methods for Inference in Multiple Change-points Models. With Arnaud Doucet and
Christophe Andrieu. 2011. Submitted. [.pdf]
- Efficient Bayesian Inference for Switching State-Space
Models using Particle Markov chain Monte Carlo methods. With Arnaud Doucet and
Christophe Andrieu. Bristol Statistics Research Report 10:04. Submitted. [arXiv]
- Bayesian Learning of Noisy Markov Decision Processes. With Sumeet Singh and
Nicolas Chopin. Technical Report CUED/F-INFENG/TR-647, University of
Cambridge. 2010. Submitted.
Journal
Publications
- Linear
variance bounds for particle approximations of time-homogeneous Feynman
Kac formulae.
With Nikolas Kantas and Ajay Jasra. Stochastic Processes and their
Applications. 2012. [arXiv]
[Journal]
- Sequential
Monte Carlo samplers: error bounds and insensitivity to initial conditions. Stochastic Analysis
and Applications. To appear. [.pdf]
- Monte Carlo filtering
of piecewise-deterministic processes. With Adam Johansen and Simon Godsill. Journal of
Computational and Graphical Statistics. [Preprint]
- Auxiliary Particle
Implementation of the Probability Hypothesis Density Filter With Sumeet Singh and
Simon Godsill. IEEE Transactions on Aerospace and Electronic
Systems. [Preprint]
Discussion
- Discussion of Particle Markov Chain Monte Carlo
methods by Andrieu, Doucet and Holenstein, Journal of the Royal
Statistical Society Series B. [.pdf]
Book Chapters
- Recent Developments in Auxiliary Particle Filtering. With Adam Johansen. In
Barber, Cemgil and Chiappa (editors), Inference and Learning in Dynamic Models,
Cambridge University Press. To Appear. [.pdf]
- An approximate
likelihood method for estimating the static parameters in multi-target
tracking models.
In Barber, Cemgil and Chiappa (editors), Inference and Learning in Dynamic Models,
Cambridge University Press. To Appear. [.pdf]
- Bayesian Statistical Methods for Audio and Music
Processing.
With Taylan Cemgil, Simon Godsill and Paul Peeling. In O'Hagan and West
(editors), The Oxford Handbook of Applied Bayesian Analysis. Oxford
University Press. To Appear. [.pdf]
Collaborators