Sergios Agapiou

441 total citations
10 papers, 195 citations indexed

About

Sergios Agapiou is a scholar working on Artificial Intelligence, Statistics and Probability and Mathematical Physics. According to data from OpenAlex, Sergios Agapiou has authored 10 papers receiving a total of 195 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Statistics and Probability and 3 papers in Mathematical Physics. Recurrent topics in Sergios Agapiou's work include Gaussian Processes and Bayesian Inference (6 papers), Statistical Methods and Inference (4 papers) and Markov Chains and Monte Carlo Methods (3 papers). Sergios Agapiou is often cited by papers focused on Gaussian Processes and Bayesian Inference (6 papers), Statistical Methods and Inference (4 papers) and Markov Chains and Monte Carlo Methods (3 papers). Sergios Agapiou collaborates with scholars based in Cyprus, United Kingdom and Spain. Sergios Agapiou's co-authors include Andrew M. Stuart, Omiros Papaspiliopoulos, Daniel Sanz-Alonso, Stig Larsson, Johnathan M. Bardsley, Masoumeh Dashti, Yuanxiang Zhang, Martin Burger, Tapio Helin and Peter Mathé and has published in prestigious journals such as Scientific Reports, The Annals of Statistics and Statistical Science.

In The Last Decade

Sergios Agapiou

10 papers receiving 180 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sergios Agapiou Cyprus	6	98	96	59	50	19	10	195
Daniel Sanz-Alonso United States	8	72 0.7×	111 1.2×	31 0.5×	13 0.3×	19 1.0×	27	227
Bert van Es Netherlands	12	225 2.3×	105 1.1×	38 0.6×	23 0.5×	12 0.6×	23	341
Xiaoqian Sun United States	10	215 2.2×	65 0.7×	37 0.6×	10 0.2×	9 0.5×	27	319
Nicola Bruti‐Liberati Australia	6	32 0.3×	33 0.3×	35 0.6×	34 0.7×	24 1.3×	10	352
Lincheng Zhao China	9	140 1.4×	60 0.6×	18 0.3×	50 1.0×	22 1.2×	19	300
Alessio Spantini United States	5	45 0.5×	75 0.8×	52 0.9×	7 0.1×	10 0.5×	6	152
Daniel Rudolf Germany	10	110 1.1×	58 0.6×	29 0.5×	24 0.5×	13 0.7×	41	279
Bernard Garel France	11	127 1.3×	96 1.0×	17 0.3×	9 0.2×	9 0.5×	23	270
Bamdad Hosseini United States	7	16 0.2×	68 0.7×	35 0.6×	20 0.4×	45 2.4×	23	217
P. Hall Australia	9	168 1.7×	76 0.8×	30 0.5×	5 0.1×	11 0.6×	17	322

Countries citing papers authored by Sergios Agapiou

Since Specialization

Citations

This map shows the geographic impact of Sergios Agapiou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sergios Agapiou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergios Agapiou more than expected).

Fields of papers citing papers by Sergios Agapiou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sergios Agapiou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sergios Agapiou. The network helps show where Sergios Agapiou may publish in the future.

Co-authorship network of co-authors of Sergios Agapiou

This figure shows the co-authorship network connecting the top 25 collaborators of Sergios Agapiou. A scholar is included among the top collaborators of Sergios Agapiou based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sergios Agapiou. Sergios Agapiou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Agapiou, Sergios, et al.. (2024). Adaptive inference over Besov spaces in the white noise model using p-exponential priors. Bernoulli. 30(3). 1 indexed citations

Agapiou, Sergios & Ismaël Castillo. (2024). Heavy-tailed Bayesian nonparametric adaptation. The Annals of Statistics. 52(4). 1 indexed citations

Agapiou, Sergios & Peter Mathé. (2022). Designing truncated priors for direct and inverse Bayesian problems. Electronic Journal of Statistics. 16(1). 4 indexed citations

Agapiou, Sergios, Anastassia Baxevani, Christos Nicolaides, et al.. (2021). Modeling the first wave of Covid-19 pandemic in the Republic of Cyprus. Scientific Reports. 11(1). 7342–7342. 4 indexed citations

Agapiou, Sergios, Martin Burger, Masoumeh Dashti, & Tapio Helin. (2018). Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems. Inverse Problems. 34(4). 45002–45002. 18 indexed citations

Agapiou, Sergios, Omiros Papaspiliopoulos, Daniel Sanz-Alonso, & Andrew M. Stuart. (2017). Importance Sampling: Intrinsic Dimension and Computational Cost. Statistical Science. 32(3). 74 indexed citations

Agapiou, Sergios, Johnathan M. Bardsley, Omiros Papaspiliopoulos, & Andrew M. Stuart. (2014). Analysis of the Gibbs Sampler for Hierarchical Inverse Problems. SIAM/ASA Journal on Uncertainty Quantification. 2(1). 511–544. 26 indexed citations

Agapiou, Sergios, Stig Larsson, & Andrew M. Stuart. (2013). Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems. Stochastic Processes and their Applications. 123(10). 3828–3860. 42 indexed citations

Agapiou, Sergios, Andrew M. Stuart, & Yuanxiang Zhang. (2013). Bayesian posterior contraction rates for linear severely ill-posed inverse problems. Journal of Inverse and Ill-Posed Problems. 22(3). 297–321. 16 indexed citations

10.

Agapiou, Sergios, Stig Larsson, & Andrew M. Stuart. (2012). POSTERIOR CONSISTENCY OF THE BAYESIAN APPROACH TO LINEAR ILL-POSED INVERSE PROBLEMS. Chalmers Publication Library (Chalmers University of Technology). 1–30. 9 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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