Lampros Bouranis - Research

Research profiles

My Google Scholar profile.
My ResearchGate profile.

ORCID: 0000-0002-1291-2192

Research interests

• Markov chain Monte Carlo methods and applications.
• Bayesian statistics – model selection; evidence/marginal likelihood estimation.
• Intractable likelihoods.
• Statistical network analysis: Bayesian inference for statistical network models and applications in social network analysis.
• Composite likelihood inference – applications to Exponential random graph models.
• Stochastic epidemiology.
• Software implementation.

Preprints

Bouranis, L., Demiris, N., Kalogeropoulos, K. and Ntzoufras, I. (2022).
Bayesian analysis of diffusion-driven multi-type epidemic models with application to COVID-19.
arXiv

Publications in peer-reviewed journals

Bouranis, L. (2023).
Bernadette: Bayesian Inference and Model Selection for Stochastic Epidemics in R.
Journal of Open Source Software, 8(89): 5612. DOI: 10.21105/joss.05612 Link

Caimo, A. , Bouranis, L., Krause, R. and Friel, N. (2022).
Statistical Network Analysis with Bergm.
Journal of Statistical Software, 104(1):1-23. DOI: 10.18637/jss.v104.i01 Link | arXiv

Bouranis, D., Gasparatos, D., Zechmann, B., Bouranis, L. and Chorianopoulou S. (2018).
The effect of granular commercial fertilizers containing elemental sulfur on wheat yield under Mediterranean conditions.
Plants, 8(1):2. DOI: 10.3390/plants8010002. Link

Bouranis, L., Friel, N., and Maire, F. (2018).
Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo.
Computational Statistics and Data Analysis, 128:221-241. DOI: 10.1016/j.csda.2018.07.005. Link | arXiv

Bouranis, L., Friel, N., and Maire, F. (2018).
Bayesian model selection for exponential random graph models via adjusted pseudolikelihoods.
Journal of Computational and Graphical Statistics, 27(3):516-528. DOI: 10.1080/10618600.2018.1448832. Link | arXiv

Bouranis, L., Friel, N., and Maire, F. (2017).
Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution.
Social Networks, 50:98-108. DOI: 10.1016/j.socnet.2017.03.013. Link | arXiv

Bouranis D., Chorianopoulou S., Bouranis, L. (2014).
Modelling the trends of nutrient concentration dynamics in S-deprived young maize plants.
Journal of Plant Nutrition, 37(13):2128-2143. DOI: 10.1080/01904167.2014.920372. Link

Bouranis D., Chorianopoulou S., Bouranis, L. (2014).
A power function based approach for the assessment of the sulfate deprivation impact on nutrient allocation in young maize plants.
Journal of Plant Nutrition, 37(5):704-722. DOI: 10.1080/01904167.2013.873455. Link

Bouranis, L., Sperrin M., Greystoke A., Dive C., Renehan AG. (2013).
The interaction between prognostic and pharmacodynamic biomarkers.
Br J Cancer, 109(7):1782-1785. DOI: 10.1038/bjc.2013.527. Link