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Found 7 papers, 5 papers with code
Fair Clustering: A Causal Perspective
Our approach enables the specification of the causal fairness metrics that should be minimised.
The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions
Mutual information quantifies the dependence between two random variables and remains invariant under diffeomorphisms.
Genomic reproducibility in the bioinformatics era
In biomedical research, validation of a new scientific discovery is tied to the reproducibility of its experimental results.
Beyond Normal: On the Evaluation of Mutual Information Estimators
Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology.
Practical and scalable simulations of non-Markovian stochastic processes
While analytic solutions often cannot be derived, existing simulation frameworks can generate stochastic trajectories compatible with the dynamical laws underlying the random phenomena.
High-Dimensional Inference in Bayesian Networks
Inference of the marginal probability distribution is defined as the calculation of the probability of a subset of the variables and is relevant for handling missing data and hidden variables.
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG
The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data.
