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Found 7 papers, 1 papers with code
Motif-based tests for bipartite networks
Based on these results, we define a goodness-of-fit test for the B-EDD model and propose a family of tests for network comparisons.
Statistics Theory Statistics Theory
Accounting for missing actors in interaction network inference from abundance data
Network inference aims at unraveling the dependency structure relating jointly observed variables.
Applications Computation
Variational inference for sparse network reconstruction from count data
We adopt a different stance by relying on a latent model where we directly model counts by means of Poisson distributions that are conditional to latent (hidden) Gaussian correlated variables.
Methodology
Variational inference for probabilistic Poisson PCA
A typical example is the joint observation of the respective abundances of a set of species in a series of sites, aiming to understand the co-variations between these species.
Methodology
Exact Bayesian inference for off-line change-point detection in tree-structured graphical models
The multivariate distribution of the observations is supposed to follow a graphical model, whose graph and parameters are affected by abrupt changes throughout time.
Exact and approximate inference in graphical models: variable elimination and beyond
We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models.
A closed-form approach to Bayesian inference in tree-structured graphical models
To this aim, we restrict the set of considered graphs to mixtures of spanning trees.
