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20072026
most citedOn adaptive resampling strategies for sequential Monte Carlo methods

181 citations · 755 across the 107 of their papers we have counts for

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Showing 2021Show all

12 papers · 1 filter

stat.CO2021

Unbiased Parameter Inference for a Class of Partially Observed Levy-Process Models

Hamza Ruzayqat, Ajay Jasra

We consider the problem of static Bayesian inference for partially observed Levy-process models. We develop a methodology which allows one to infer static parameters and some state…

math.PR2021★ 2 cited

On the Stability of Positive Semigroups

Pierre del Moral, Emma Horton, Ajay Jasra

The stability and contraction properties of positive integral semigroups on Polish spaces are investigated. Our novel analysis is based on the extension of V-norm contraction metho…

stat.CO2021

A Lagged Particle Filter for Stable Filtering of certain High-Dimensional State-Space Models

Hamza Ruzayqat, Aimad Er-Raiy, Alexandros Beskos +3

We consider the problem of high-dimensional filtering of state-space models (SSMs) at discrete times. This problem is particularly challenging as analytical solutions are typically…

stat.ME2021★ 1 cited

Unbiased Estimation of the Hessian for Partially Observed Diffusions

Neil K. Chada, Ajay Jasra, Fangyuan Yu

In this article we consider the development of unbiased estimators of the Hessian, of the log-likelihood function with respect to parameters, for partially observed diffusion proce…

math.NA2021

Multilevel Estimation of Normalization Constants Using the Ensemble Kalman-Bucy Filter

Hamza Ruzayqat, Neil K. Chada, Ajay Jasra

In this article we consider the application of multilevel Monte Carlo, for the estimation of normalizing constants. In particular we will make use of the filtering algorithm, the e…

math.NA2021

Improved Efficiency of Multilevel Monte Carlo for Stochastic PDE through Strong Pairwise Coupling

Neil K. Chada, Håkon Hoel, Ajay Jasra +1

Multilevel Monte Carlo (MLMC) has become an important methodology in applied mathematics for reducing the computational cost of weak approximations. For many problems, it is well-k…