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<h1 class="title toc-ignore">Lampros Bouranis - Research</h1>
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<p><link rel="stylesheet" href="styles.css" type="text/css">
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<div id="research-profiles" class="section level2">
<h2>Research profiles</h2>
<p><i class="ai ai-google-scholar-square ai-1x"></i> My <a
href="https://scholar.google.com/citations?hl=en&user=Qcvw8zoAAAAJ">Google
Scholar</a> profile.<br />
<i class="ai ai-researchgate-square ai-1x"></i> My <a
href="https://www.researchgate.net/profile/Lampros_Bouranis">ResearchGate</a>
profile.</p>
<p><strong>ORCID:</strong> 0000-0002-1291-2192</p>
</div>
<div id="research-interests" class="section level2">
<h2>Research interests</h2>
<ul>
<li>Markov chain Monte Carlo methods and applications.</li>
<li>Bayesian statistics – model selection; evidence/marginal likelihood
estimation.</li>
<li>Evidence synthesis.</li>
<li>Intractable likelihoods.</li>
<li>Network meta-analysis.</li>
<li>Statistical network analysis: Bayesian inference for statistical
network models and applications in social network analysis.</li>
<li>Composite likelihood inference – applications to Exponential random
graph models.</li>
<li>Stochastic epidemiology.</li>
<li>Software implementation.</li>
</ul>
</div>
<div id="preprints" class="section level2">
<h2>Preprints</h2>
<p><strong>Bouranis, L.</strong>, Demiris, N., Kalogeropoulos, K. and
Ntzoufras, I. (2022).<br />
<strong>Bayesian analysis of diffusion-driven multi-type epidemic models
with application to COVID-19</strong>.<br />
<a href="https://arxiv.org/abs/2211.15229">arXiv</a></p>
</div>
<div id="publications-in-peer-reviewed-journals" class="section level2">
<h2>Publications in peer-reviewed journals</h2>
<p><strong>Bouranis, L.</strong> (2023).<br />
<strong>Bernadette: Bayesian Inference and Model Selection for
Stochastic Epidemics in R</strong>.<br />
<em>Journal of Open Source Software</em>, 8(89): 5612. DOI:
10.21105/joss.05612 <a
href="https://joss.theoj.org/papers/10.21105/joss.05612">Link</a></p>
<p>Caimo, A. , <strong>Bouranis, L.</strong>, Krause, R. and Friel, N.
(2022).<br />
<strong>Statistical Network Analysis with Bergm</strong>.<br />
<em>Journal of Statistical Software</em>, 104(1):1-23. DOI:
10.18637/jss.v104.i01 <a
href="https://www.jstatsoft.org/article/view/v104i01">Link</a> | <a
href="https://arxiv.org/abs/2104.02444">arXiv</a></p>
<p>Bouranis, D., Gasparatos, D., Zechmann, B., <strong>Bouranis,
L.</strong> and Chorianopoulou S. (2018).<br />
<strong>The effect of granular commercial fertilizers containing
elemental sulfur on wheat yield under Mediterranean
conditions</strong>.<br />
<em>Plants</em>, 8(1):2. DOI: 10.3390/plants8010002. <a
href="https://www.ncbi.nlm.nih.gov/pubmed/30577492">Link</a></p>
<p><strong>Bouranis, L.</strong>, Friel, N., and Maire, F. (2018).<br />
<strong>Model comparison for Gibbs random fields using noisy reversible
jump Markov chain Monte Carlo</strong>.<br />
<em>Computational Statistics and Data Analysis</em>, 128:221-241. DOI:
10.1016/j.csda.2018.07.005. <a
href="https://www.sciencedirect.com/science/article/pii/S0167947318301713">Link</a>
| <a href="https://arxiv.org/abs/1712.05358">arXiv</a></p>
<p><strong>Bouranis, L.</strong>, Friel, N., and Maire, F. (2018).<br />
<strong>Bayesian model selection for exponential random graph models via
adjusted pseudolikelihoods</strong>.<br />
<em>Journal of Computational and Graphical Statistics</em>,
27(3):516-528. DOI: 10.1080/10618600.2018.1448832. <a
href="https://www.tandfonline.com/doi/abs/10.1080/10618600.2018.1448832?journalCode=ucgs20">Link</a>
| <a href="https://arxiv.org/abs/1706.06344">arXiv</a></p>
<p><strong>Bouranis, L.</strong>, Friel, N., and Maire, F. (2017).<br />
<strong>Efficient Bayesian inference for exponential random graph models
by correcting the pseudo-posterior distribution</strong>.<br />
<em>Social Networks</em>, 50:98-108. DOI: 10.1016/j.socnet.2017.03.013.
<a
href="https://www.sciencedirect.com/science/article/pii/S0378873315301489">Link</a>
| <a href="https://arxiv.org/abs/1510.00934">arXiv</a></p>
<p>Bouranis D., Chorianopoulou S., <strong>Bouranis, L.</strong>
(2014).<br />
<strong>Modelling the trends of nutrient concentration dynamics in
S-deprived young maize plants</strong>.<br />
<em>Journal of Plant Nutrition</em>, 37(13):2128-2143. DOI:
10.1080/01904167.2014.920372. <a
href="https://www.tandfonline.com/doi/abs/10.1080/01904167.2014.920372">Link</a></p>
<p>Bouranis D., Chorianopoulou S., <strong>Bouranis, L.</strong>
(2014).<br />
<strong>A power function based approach for the assessment of the
sulfate deprivation impact on nutrient allocation in young maize
plants</strong>.<br />
<em>Journal of Plant Nutrition</em>, 37(5):704-722. DOI:
10.1080/01904167.2013.873455. <a
href="https://www.tandfonline.com/doi/abs/10.1080/01904167.2013.873455">Link</a></p>
<p><strong>Bouranis, L.</strong>, Sperrin M., Greystoke A., Dive C.,
Renehan AG. (2013).<br />
<strong>The interaction between prognostic and pharmacodynamic
biomarkers</strong>.<br />
<em>Br J Cancer</em>, 109(7):1782-1785. DOI: 10.1038/bjc.2013.527. <a
href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3790178/">Link</a></p>
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