Alexandros Beskos

Alexandros Beskos

University College London

H-index: 24

Europe-United Kingdom

About Alexandros Beskos

Alexandros Beskos, With an exceptional h-index of 24 and a recent h-index of 20 (since 2020), a distinguished researcher at University College London, specializes in the field of Bayesian Statistics, Computational Statistics, Statistical Modelling, Financial Statistics, Biostatistics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market

ParticleDA. jl v. 1.0: a distributed particle-filtering data assimilation package

ParticleDA. jl v. 1.0: A real-time data assimilation software platform

Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition

Unbiased Estimation using a Class of Diffusion Processes

Graph Sphere: From Nodes to Supernodes in Graphical Models

Parameter Inference for Degenerate Diffusion Processes

Alexandros Beskos Information

University

Position

Reader in Statistics

Citations(all)

3769

Citations(since 2020)

1746

Cited By

2816

hIndex(all)

24

hIndex(since 2020)

20

i10Index(all)

37

i10Index(since 2020)

31

Email

University Profile Page

University College London

Google Scholar

View Google Scholar Profile

Alexandros Beskos Skills & Research Interests

Bayesian Statistics

Computational Statistics

Statistical Modelling

Financial Statistics

Biostatistics

Top articles of Alexandros Beskos

Title

Journal

Author(s)

Publication Date

Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

arXiv preprint arXiv:2403.13489

Yuga Iguchi

Ajay Jasra

Mohamed Maama

Alexandros Beskos

2024/3/20

Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market

The Annals of Applied Statistics

Beatrice Franzolini

Alexandros Beskos

Maria De Iorio

Warrick Poklewski Koziell

Karolina Grzeszkiewicz

2024/3

ParticleDA. jl v. 1.0: a distributed particle-filtering data assimilation package

Geoscientific Model Development

Daniel Giles

Matthew M Graham

Mosè Giordano

Tuomas Koskela

Alexandros Beskos

...

2024/3/28

ParticleDA. jl v. 1.0: A real-time data assimilation software platform

Geoscientific Model Development Discussions

Daniel Giles

Matthew M Graham

Mosè Giordano

Tuomas Koskela

Alexandros Beskos

...

2023/3/21

Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition

arXiv preprint arXiv:2312.04444

Yuga Iguchi

Alexandros Beskos

2023/12/7

Unbiased Estimation using a Class of Diffusion Processes

Journal of Computational Physics

Hamza Ruzayqat

Alexandros Beskos

Dan Crisan

Ajay Jasra

Nikolas Kantas

2023/1/1

Graph Sphere: From Nodes to Supernodes in Graphical Models

arXiv preprint arXiv:2310.11741

Willem van den Boom

Maria De Iorio

Alexandros Beskos

Ajay Jasra

2023/10/18

Parameter Inference for Degenerate Diffusion Processes

arXiv preprint arXiv:2307.16485

Yuga Iguchi

Alexandros Beskos

Matthew Graham

2023/7/31

Sequential Markov Chain Monte Carlo for Lagrangian Data Assimilation with Applications to Unknown Data Locations

Quarterly Journal of the Royal Meteorological Society

Hamza Ruzayqat

Alexandros Beskos

Dan Crisan

Ajay Jasra

Nikolas Kantas

2024/9/1

The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models

Journal of Computational and Graphical Statistics

Willem van den Boom

Alexandros Beskos

Maria De Iorio

2022/11

Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo

Statistics and Computing

Willem van den Boom

Ajay Jasra

Maria De Iorio

Alexandros Beskos

Johan G Eriksson

2022/6

Online smoothing for diffusion processes observed with noise

Journal of Computational and Graphical Statistics

Shouto Yonekura

Alexandros Beskos

2022/11

MCMC algorithms for posteriors on matrix spaces

Journal of Computational and Graphical Statistics

Alexandros Beskos

Kengo Kamatani

2022/10/12

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

SIAM/ASA Journal on Uncertainty Quantification

Hamza Ruzayqat

Aimad Er-Raiy

Alexandros Beskos

Dan Crisan

Ajay Jasra

...

2022/9/30

Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions

arXiv preprint arXiv:2211.16384

Yuga Iguchi

Alexandros Beskos

Matthew M Graham

2022/11/29

Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models

Journal of the Royal Statistical Society, Series B

Matthew M Graham

Alexandre H Thiery

Alexandros Beskos

2022/9/30

A Bayesian framework for genome-wide inference of DNA methylation levels

arXiv preprint arXiv:2211.07311

Marcel Hirt

Axel Finke

Alexandros Beskos

Petros Dellaportas

Stephan Beck

...

2022/11/14

A 4D-Var method with flow-dependent background covariances for the shallow-water equations

arXiv preprint arXiv:1710.11529

Daniel Paulin

Ajay Jasra

Alexandros Beskos

Dan Crisan

2017/10/31

Score-Based Parameter Estimation for a Class of Continuous-Time State Space Models

SIAM Journal on Scientific Computing

Alexandros Beskos

Dan Crisan

Ajay Jasra

Nikolas Kantas

Hamza Ruzayqat

2021

Asymptotic Analysis of Model Selection Criteria for General Hidden Markov Models

Stochastic Processes and their Applications

Shouto Yonekura

Alexandros Beskos

Sumeetpal S Singh

2021/2/1

See List of Professors in Alexandros Beskos University(University College London)

Co-Authors

H-index: 78
Andrew M Stuart

Andrew M Stuart

California Institute of Technology

H-index: 74
Mark Girolami

Mark Girolami

University of Cambridge

H-index: 52
Paul Fearnhead

Paul Fearnhead

Lancaster University

H-index: 39
Dan Crisan

Dan Crisan

Imperial College London

H-index: 34
Frank J. Pinski

Frank J. Pinski

University of Cincinnati

H-index: 33
Ajay Jasra

Ajay Jasra

King Abdullah University of Science and Technology

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