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Research highlights

Here are links to just a few of the group's recent research outputs in statistical theory and methodology
Monte Carlo methods and related theory
Particle Markov chain Monte Carlo methods
by Christophe Andrieu, Arnaud Doucet and Roman Holenstein

Linear variance bounds for particle approximations of time-homogeneous Feynman-Kac formulae
by Nick Whiteley, Nikolas Kantas and Ajay Jasra

The pseudo-marginal approach for efficient Monte Carlo computations
by Christophe Andrieu and Gareth Roberts

Nonparametric methods in regression and time series
Spectral estimation for locally stationary time series with missing observations
by Marina I. Knight, Matthew A. Nunes and Guy P. Nason

Quadratic Programming and Penalised Regression
by Andrew D. A. C. Smith

Nonparametric regression, confidence regions and regularization
by Laurie Davies, Arne Kovac and Monika Meise

Multiscale methods for data on graphs and irregular multidimensional situations
by Martin Jansen, Guy Nason and Bernard Silverman

Bayesian analysis and graphical modelling
Graphical Models for Inference Under Outcome-Dependent Sampling
by Vanessa Didelez, Svend Kreiner and Niels Keiding

Sampling decomposable graphs using a Markov chain on junction trees
by Peter Green and Alun Thomas

Reified Bayesian modelling and inference for physical systems
by Michael Goldstein and Jonathan Rougier

Optimistic Bayesian Sampling in Contextual-Bandit Problems
by Benedict C. May, Nathan Korda, Anthony Lee and David S. Leslie

Bayesian consistency for regression models under a supremum distance
by Fei Xiang and Stephen Walker

Analyticity, Convergence, and Convergence Rate of Recursive Maximum-Likelihood Estimation in Hidden Markov Models
by Vladislav B. Tadic

Other topics
Identifying Sources of Variation and the Flow of Information in Biochemical Networks
by Clive Bowsher and Peter Swain

Theoretical properties of Cook's PFC dimension reduction algorithm for linear regression
by Oliver Johnson