Sergios Agapiou

I am an Associate Professor at the Department of Mathematics and Statistics, University of Cyprus.

My work lies at the interface of Differential Equations and Probability/Statistics. More concretely, my research focuses on Bayesian nonparametric Statistics (including but not limited to inverse problems), and I am interested in both theoretical questions, such as the asymptotic performance of posterior distributions in the infinitely-informative data limit, as well as computational questions, such as the design and analysis of sampling algorithms in high dimensions.

I currently serve as a member of the steering committee of the International Symposium on Nonparametric Statistics ISNPS. I also serve as Associate Editor of the journal Bayesian Analysis.

View my CV

Department of Mathematics and Statistics

University of Cyprus

1 University Avenue, 2109 Nicosia, Cyprus

agapiou(dot)sergios(at)ucy(dot)ac(dot)cy

Publications

[14] Heavy-tailed and Horseshoe priors for regression and sparse Besov rates

S. Agapiou, I. Castillo and P. Egels

To appear in Bernoulli, arXiv preprint

[13] Heavy-tailed Bayesian nonparametric adaptation

S. Agapiou and I. Castillo

The Annals of Statistics, volume 52, number 4 (2024), pages 1433-1459

Online publication, published pdf, arXiv, code

[12] Adaptive inference over Besov spaces in the white noise model using p-exponential priors

S. Agapiou and A. Savva

Bernoulli, volume 30, number 3 (2024), pages 2275-2300

Online publication, arXiv

[11] Laplace priors and spatial inhomogeneity in Bayesian inverse problems

S. Agapiou and S. Wang

Bernoulli, volume 30, number 2 (2024), pages 878-910

Online publication, arXiv

[10] Designing truncated priors for direct and inverse Bayesian problems

S. Agapiou and P. Mathé

Electronic Journal of Statistics, volume 16, number 1 (2022), pages 158-200

Online publication, arXiv

[9] Rates of contraction of posterior distributions based on p-exponential priors

S. Agapiou, M. Dashti and T. Helin

Bernoulli, volume 27, number 3 (2021), pages 1616-1642

Online publication (supplement), arXiv

[8] Modeling the first wave of Covid-19 pandemic in the Republic of Cyprus

S. Agapiou, A. Anastasiou, A. Baxevani, T. Christofides, E. Constantinou, G. Hadjigeorgiou, C. Nicolaides, G. Nikolopoulos and K. Fokianos

Scientific Reports, 11, Article number: 7342 (2021)

Online publication, arXiv

[7] Sparsity promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems

S. Agapiou, M. Burger, M. Dashti and T. Helin

Inverse Problems, volume 34, number 4 (2018)

Online publication, arXiv

[6] Posterior contraction in Bayesian inverse problems under Gaussian priors

S. Agapiou and P. Mathé

New trends in parameter identification for mathematical models, Springer series Trends in Mathematics (2018), pages 1-19

Online publication, arXiv

[5] Unbiased Monte Carlo: posterior estimation for intractable/infinite-dimensional models

S. Agapiou, G. O. Roberts and S. J. Vollmer

Bernoulli, volume 24, number 3 (2018), pages 1726-1786

Online publication (supplement), arXiv

[4] Importance Sampling: Intrinsic Dimension and Computational Cost

S. Agapiou, D. Sanz-Alonso, Omiros Papaspiliopoulos and A. M. Stuart

Statistical Science, volume 32, number 3 (2017), pages 405-431

Online publication (supplement), arXiv

[3] Analysis of the Gibbs sampler for hierarchical inverse problems

S. Agapiou, J. M. Bardsley, O. Papaspiliopoulos and A. M. Stuart

SIAM/ASA Journal on Uncertainty Quantification, volume 2, issue 1 (2014), pages 511-544

Online publication, arXiv

[2] Bayesian posterior contraction rates for linear severely ill-posed inverse problems

S. Agapiou, A. M. Stuart and Yuan-Xiang Zhang

Journal of Inverse and Ill-Posed Problems, volume 22, issue 3 (2014), pages 297-321

Online publication, arXiv

[1] Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems

S. Agapiou, S. Larsson and A. M. Stuart

Stochastic Processes and their Applications, volume 123, issue 10 (2013), pages 3828-3860

Online publication, arXiv

Theses

PhD: Aspects of Bayesian inverse problems
MSc: A classical and a Bayesian approach to linear ill-posed inverse problems
Diploma (in Greek): Stable and Infinitely Divisible distributions

Selected Talks

A new way of achieving adaptation (slides, video) - ISBA-BNP Webinar, 24 May 2023
Adaptive rates of contraction for spatially inhomogeneous unknowns - Statistics seminar (Athens University of Economics and Business), 18 May 2022
Gauss vs Laplace rates of contraction under Besov regularity - International Bayes Club (online), 03 December 2020
Statistical modelling of the effective reproduction number of COVID-19 in Cyprus (in greek) - 6th Public Health Day (Cyprus University of Technology), 29 September 2020
Edge-preserving Bayesian inversion - Bayesian and nonlinear inverse problems (Lorentz Center, Leiden), 28 August - 1 September 2017
The intrinsic dimension of Importance Sampling- Reading-Warwick Data Assimilation Day (University of Reading), 23 June 2015
Bayesian posterior contraction rates via classical regularization techniques - Applied Inverse Problems conference (Helsinki), 25-29 May 2015
Practical unbiased Monte Carlo for intractable models - Statistics seminar (Imperial), 20 March 2015
Analysis of the Gibbs Sampler for hierarchical inverse problems - Eleventh MC-QMC conference (Leuven), 6-11 April 2014
Posterior contraction rates for Bayesian inverse problems - Stochastic Analysis Seminar (Oxford University), 21 January 2013

Sergios Agapiou

Publications

[14] Heavy-tailed and Horseshoe priors for regression and sparse Besov rates

[13] Heavy-tailed Bayesian nonparametric adaptation

[12] Adaptive inference over Besov spaces in the white noise model using p-exponential priors

[11] Laplace priors and spatial inhomogeneity in Bayesian inverse problems

[10] Designing truncated priors for direct and inverse Bayesian problems

[9] Rates of contraction of posterior distributions based on p-exponential priors

[8] Modeling the first wave of Covid-19 pandemic in the Republic of Cyprus

[7] Sparsity promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems

[6] Posterior contraction in Bayesian inverse problems under Gaussian priors

[5] Unbiased Monte Carlo: posterior estimation for intractable/infinite-dimensional models

[4] Importance Sampling: Intrinsic Dimension and Computational Cost

[3] Analysis of the Gibbs sampler for hierarchical inverse problems

[2] Bayesian posterior contraction rates for linear severely ill-posed inverse problems

[1] Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems

Theses

Selected Talks

Teaching