166 citations · 309 across the 26 of their papers we have counts for
8 papers · 1 filter
Conditional sequential Monte Carlo in high dimensions
Axel Finke, Alexandre H. Thiery
The iterated conditional sequential Monte Carlo (i-CSMC) algorithm from Andrieu, Doucet and Holenstein (2010) is an MCMC approach for efficiently sampling from the joint posterior…
Manifold lifting: scaling MCMC to the vanishing noise regime
Khai Xiang Au, Matthew M. Graham, Alexandre H. Thiery
Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures. These pathologies naturally oc…
Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models
Matthew M. Graham, Alexandre H. Thiery, Alexandros Beskos
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the comput…
A scalable optimal-transport based local particle filter
Matthew M. Graham, Alexandre H. Thiery
Filtering in spatially-extended dynamical systems is a challenging problem with significant practical applications such as numerical weather prediction. Particle filters allow asym…
On Coupling Particle Filter Trajectories
Deborshee Sen, Alexandre Thiery, Ajay Jasra
Particle filters are a powerful and flexible tool for performing inference on state-space models. They involve a collection of samples evolving over time through a combination of s…
Asymptotic Analysis of the Random-Walk Metropolis Algorithm on Ridged Densities
Alexandros Beskos, Gareth Roberts, Alexandre Thiery +1
In this paper we study the asymptotic behavior of the Random-Walk Metropolis algorithm on probability densities with two different `scales', where most of the probability mass is d…
On the efficiency of pseudo-marginal random walk Metropolis algorithms
Chris Sherlock, Alexandre H. Thiery, Gareth O. Roberts +1
We examine the behaviour of the pseudo-marginal random walk Metropolis algorithm, where evaluations of the target density for the accept/reject probability are estimated rather tha…
On the Convergence of Adaptive Sequential Monte Carlo Methods
Alexandros Beskos, Ajay Jasra, Nikolas Kantas +1
In several implementations of Sequential Monte Carlo (SMC) methods it is natural, and important in terms of algorithmic efficiency, to exploit the information of the history of the…