Sergios Agapiou
TitleCited byYear
Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems
S Agapiou, S Larsson, AM Stuart
Stochastic Processes and their Applications 123 (10), 3828-3860, 2013
652013
Importance sampling: Intrinsic dimension and computational cost
S Agapiou, O Papaspiliopoulos, D Sanz-Alonso, AM Stuart
Statistical Science 32 (3), 405-431, 2017
38*2017
Analysis of the Gibbs sampler for hierarchical inverse problems
S Agapiou, JM Bardsley, O Papaspiliopoulos, AM Stuart
SIAM/ASA Journal on Uncertainty Quantification 2 (1), 511-544, 2014
312014
Bayesian posterior contraction rates for linear severely ill-posed inverse problems
S Agapiou, AM Stuart, YX Zhang
Journal of Inverse and Ill-posed Problems 22 (3), 297-321, 2014
282014
Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems
S Agapiou, M Burger, M Dashti, T Helin
Inverse Problems 34 (4), 045002, 2018
82018
Aspects of bayesian inverse problems
S Agapiou
University of Warwick, 2013
82013
Unbiased Monte Carlo: posterior estimation for intractable/infinite-dimensional models
S Agapiou, GO Roberts, SJ Vollmer
Bernoulli 24 (3), 1726-1786, 2018
7*2018
Posterior Contraction in Bayesian Inverse Problems Under Gaussian Priors
S Agapiou, P Mathé
New Trends in Parameter Identification for Mathematical Models, 1-29, 2018
4*2018
Rates of contraction of posterior distributions based on -exponential priors
S Agapiou, M Dashti, T Helin
arXiv preprint arXiv:1811.12244, 2018
2018
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Articles 1–9