2 citations · 3 across the 2 of their papers we have counts for
4 papers
Beyond time-homogeneity for continuous-time multistate Markov models
Emmett B. Kendall, Jonathan P. Williams, Gudmund H. Hermansen +2
Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov…
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…
A high-throughput analysis of ovarian cycle disruption by mixtures of aromatase inhibitors
Frederic Y. Bois, Nazanin Golbamaki-Bakhtyari, Simona Kovarich +3
Background: Combining computational toxicology with ExpoCast exposure estimates and ToxCast assay data gives us access to predictions of human health risks stemming from exposures…
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…