School of Mathematics
Wasserstein contraction of nonlinear filters. 2017.
association, ordering and convergence of resampling methods. With
Mathieu Gerber and Nicolas Chopin. 2017. [arXiv]
normalizing constants in high dimensions using inhomogeneous diffusions.
With Christophe Andrieu and James Ridgway. 2016.
estimation and allocation in the particle filter.
With Anthony Lee. 2016. [arXiv]
resampling: asymptotics for particle filters
with constrained interactions.
With Kari Heine, Taylan Cemgil and Hakan Guldas. 2014. [arXiv]
stability and instability of a distributed particle filter with local
exchange. With Kari Heine. Stochastic
Processes and their Applications. In press. [arXiv]
principal eigen-functions of non-negative kernels: particle approximations
and applications. With Nikolas Kantas. Mathematics of Operations Research. In press. [arXiv]
algorithm for approximating the second moment of the normalizing constant
estimate from a particle filter. With Svetoslav Kostov. Methodology and Computing in Applied
Probability. In press. [arXiv]
with respect to initial conditions in V-norm for nonlinear filters with
ergodic observations. With Mathieu Gerber. Journal
of Applied Probability. 2017. [arXiv]
Introduction to Twisted Particle Filters and Parameter Estimation in
Non-linear State-space Models. With Juha
Ala-Luhtala and Kari Heine. IEEE Transactions on
Signal Processing. 2016. [arXiv]
hidden Markov model for decoding and the analysis of replay in spike
trains. With Marc Box and Matt Jones. Journal of Computational
Neuroscience. 2016. [arXiv] Matlab files here.
sampling for nonhomogeneous Markov chains and hidden Markov models.
With Anthony Lee. The Annals of Applied Probability. 2016. [arXiv]
resampling for distributed sequential Monte Carlo.
With Anthony Lee. Statistical Analysis and Data Mining. 2016. [preprint]
the role of interaction in sequential Monte Carlo algorithms.
With Anthony Lee and Kari Heine. Bernoulli. 2016. [arXiv]
Matlab files here.
Particle Filters. With Anthony Lee. The Annals
of Statistics. 2014. [arXiv]
and the proofs: [.pdf]
properties of some particle filters. The Annals of
Applied Probability. 2013. [arXiv]
- Bayesian Learning of Noisy Markov
Decision Processes. With Sumeet Singh and Nicolas
Chopin. ACM TOMACS. 2013. [journal]
variance bounds for particle approximations of time-homogeneous Feynman Kac formulae. With Nikolas Kantas and Ajay Jasra.
Stochastic Processes and their Applications. 2012. [arXiv]
Monte Carlo samplers: error bounds and insensitivity to initial conditions.
2012. Stochastic Analysis and Applications. [Preprint]
- Monte Carlo filtering of
piecewise-deterministic processes. With Adam
Johansen and Simon Godsill. Journal of
Computational and Graphical Statistics. 2011. [Preprint]
- Auxiliary Particle Implementation
of the Probability Hypothesis Density Filter With
Sumeet Singh and Simon Godsill. IEEE
Transactions on Aerospace and Electronic Systems. 2010. [Preprint]
- Discussion of Particle
Markov Chain Monte Carlo methods by Andrieu,
Doucet and Holenstein, Journal of the Royal
Statistical Society Series B. [.pdf]
- Recent Developments in
Auxiliary Particle Filtering. With Adam Johansen. In
Barber, Cemgil and Chiappa
(editors), Inference and Learning in Dynamic Models, Cambridge
University Press. [.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. [.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. [.pdf]