39 citations · 63 across the 10 of their papers we have counts for
10 papers
Hypotheses testing and posterior concentration rates for semi-Markov processes
V Barbu, Ghislaine Gayraud, N. Limnios +1
In this paper, we adopt a nonparametric Bayesian approach and investigate the asymptotic behavior of the posterior distribution in continuous time and general state space semi-Mark…
Structure learning of Bayesian networks involving cyclic structures
Witold Wiecek, Frederic Y. Bois, Ghislaine Gayraud
Many biological networks include cyclic structures. In such cases, Bayesian networks (BNs), which must be acyclic, are not sound models for structure learning. Dynamic BNs can be u…
Graph_sampler: a simple tool for fully Bayesian analyses of DAG-models
Sagnik Datta, Ghislaine Gayraud, Eric Leclerc +1
Bayesian networks (BNs) are widely used graphical models usable to draw statistical inference about Directed acyclic graphs (DAGs). We presented here Graph_sampler a fast free C la…
Probabilistic generation of random networks taking into account information on motifs occurrence
Frederic Y. Bois, Ghislaine Gayraud
Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random direct…
Bayesian inference for CoVaR
Mauro Bernardi, Ghislaine Gayraud, Lea Petrella
Recent financial disasters emphasised the need to investigate the consequence associated with the tail co-movements among institutions; episodes of contagion are frequently observe…
Sharp detection of smooth signals in a high-dimensional sparse matrix with indirect observations
Cristina Butucea, Ghislaine Gayraud
We consider a matrix-valued Gaussian sequence model, that is, we observe a sequence of high-dimensional matrices of heterogeneous Gaussian random variables …