info Julien S. and Stépĥane

_bibliography/preprint.bib

@@ -1,3 +1,13 @@
+@preprint{preprint_stoehr,
+      title = {Composite likelihood inference for the Poisson log-normal model}, 
+      author = {Stoehr, Julien and Robin, Stephane},
+      year = {2024},
+	arxiv = {abs/2402.14390v1},
+  url = {https://arxiv.org/abs/2402.14390v1},
+	journal = {arXiv},
+	abstract = {Inferring parameters of a latent variable model can be a daunting task when the conditional distribution of the latent variables given the observed ones is intractable. Variational approaches prove to be computationally efficient but, possibly, lack theoretical guarantees on the estimates, while sampling based solutions are quite the opposite. Starting from already available variational approximations, we define a first Monte Carlo EM algorithm to obtain maximum likelihood estimators, focusing on the Poisson log-normal model which provides a generic framework for the analysis of multivariate count data. We then extend this algorithm to the case of a composite likelihood in order to be able to handle higher dimensional count data.}
+}
+
 @preprint{preprint_pauvert,
 abbr = {bioRxiv},
         author = {Pauvert, Charlie and Fort, Tania and Calonnec, Agnès and

_data/repositories.yml

@@ -2,3 +2,4 @@ github_repos:
   - PLN-team/PLNmodels
   - PLN-team/pyPLNmodels
   - PLN-team/slideshow
+  - PLN-team/pln-team.github.io

_pages/about_julien.md

@@ -1,7 +1,7 @@
 
 <br/>
 
-<a href="https://jchiquet.github.io">Julien Chiquet</a> is Senior Research in Statistical Learning, at Université Paris-Saclay/AgroParisTech/INRAE. He is heading the [UMR MIA Paris-Saclay](https://mia-ps.inrae.fr/), and he belongs to team [SOLsTIS](https://mia-ps.inrae.fr/solstis). He is also chief editor of [Computo](https://computo.sfds.fr), an academic journal promoting reproducibility  by leveraging technical advances in literate programming and scientific reporting.
+[Julien Chiquet](https://jchiquet.github.io) is Senior Research in Statistical Learning, at Université Paris-Saclay/AgroParisTech/INRAE. He is heading the [UMR MIA Paris-Saclay](https://mia-ps.inrae.fr/), and he belongs to team [SOLsTIS](https://mia-ps.inrae.fr/solstis). He is also chief editor of [Computo](https://computo.sfds.fr), an academic journal promoting reproducibility  by leveraging technical advances in literate programming and scientific reporting.
 
 <a href="https://orcid.org/0000-0002-3629-3429" title="ORCID"><i class="ai ai-orcid"></i></a>
 <a href="https://scholar.google.com/citations?user=FM2gRsYAAAAJ" title="Google Scholar"><i class="ai ai-google-scholar"></i></a>

_pages/about_julien_s.md

@@ -1 +1,7 @@
-to be completed
+<br/>
+
+[Julien Stoehr](https://www.ceremade.dauphine.fr/~stoehr/) is a Maître de Conférences at Université Paris-Dauphine, Université PSL. Member of the CEREMADE, his expertise lies on the development of statistical computational methods for intractable distributions, with an insight on Monte Carlo, Markov Chain Monte Carlo and Approximate Bayesian computation algorithms.
+
+<a href="https://orcid.org/0000-0002-7813-0185" title="ORCID"><i class="ai ai-orcid"></i></a>
+<a href="https://scholar.google.com/citations?user=NkJPWnMAAAAJ" title="Google Scholar"><i class="ai ai-google-scholar"></i></a>
+<a href="https://www.ceremade.dauphine.fr/~stoehr/" title="Work"><i class="fas fa-briefcase"></i></a>

_pages/about_stephane.md

@@ -1 +1,7 @@
-to be completed
+<br/>
+
+[Stéphane Robin](https://scj-robin.github.io) is professor at the Laboratoire de Probabilités, Statistique et Modélisation (LPSM), in Professor at Sorbonne Université (Paris). His specialized in statistics and their application in life sciences and ecology. His works focus on latent variable models,
+ network models and change-point detection.
+
+<a href="https://scholar.google.com/citations?user=7fBfsj0AAAAJ" title="Google Scholar"><i class="ai ai-google-scholar"></i></a>
+<a href="https://scj-robin.github.io" title="Work"><i class="fas fa-briefcase"></i></a>