G
Gareth O. Roberts
Researcher at University of Warwick
Publications - 50
Citations - 2024
Gareth O. Roberts is an academic researcher from University of Warwick. The author has contributed to research in topics: Markov chain Monte Carlo & Markov chain. The author has an hindex of 22, co-authored 50 publications receiving 1675 citations. Previous affiliations of Gareth O. Roberts include Lancaster University.
Papers
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On the efficiency of pseudo-marginal random walk Metropolis algorithms
TL;DR: In this paper, the authors examined the behavior of the pseudo-marginal random walk Metropolis algorithm, where evaluations of the target density for the accept/reject probability are estimated rather than computed precisely.
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The Zig-Zag process and super-efficient sampling for Bayesian analysis of big data
TL;DR: A new family of Monte Carlo methods based upon a multi-dimensional version of the Zig-Zag process of (Bierkens, Roberts, 2017), a continuous time piecewise deterministic Markov process is introduced.
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Particle filters for partially observed diffusions
TL;DR: In this article, the authors introduce particle filters for a class of partially-observed continuous-time dynamic models where the signal is given by a multivariate diffusion process, and they build on recent methodology for exact simulation of the diffusion process and the unbiased estimation of the transition density as described in Beskos et al. (2006).
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Optimal scalings for local Metropolis–Hastings chains on nonproduct targets in high dimensions
TL;DR: This work investigates local MCMC algorithms, namely the random-walk Metropolis and the Langevin algorithms, and identifies the optimal choice of the local step-size as a function of the dimension $n$ of the state space, asymptotically as $n\to\infty$.
Posted Content
Particle Filters for Partially Observed Diffusions
TL;DR: In this paper, the authors proposed a particle filter scheme for a class of partially-observed multivariate diffusions, which does not require approximations of the transition and/or the observation density using timediscretisations.