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Found 12 papers, 9 papers with code
Bayesian Causal Inference with Gaussian Process Networks
Simulation studies show that our approach is able to identify the effects of hypothetical interventions with non-Gaussian, non-linear observational data and accurately reflect the posterior uncertainty of the causal estimates.
Fair Clustering: A Causal Perspective
Our approach enables the specification of the causal fairness metrics that should be minimised.
A Bayesian Take on Gaussian Process Networks
Gaussian Process Networks (GPNs) are a class of directed graphical models which employ Gaussian processes as priors for the conditional expectation of each variable given its parents in the network.
The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions
To this end, we define the interventional BGe score for a mixture of observational and interventional data, where the targets and effects of intervention may be unknown.
The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networks
Learning the graphical structure of Bayesian networks is key to describing data-generating mechanisms in many complex applications but poses considerable computational challenges.
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.
Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms
To facilitate the benchmarking of different methods, we present a novel Snakemake workflow, called Benchpress for producing scalable, reproducible, and platform-independent benchmarks of structure learning algorithms for probabilistic graphical models.
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.
Efficient Sampling and Structure Learning of Bayesian Networks
Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high dimensional data, and even to facilitate causal discovery.
Partition MCMC for inference on acyclic digraphs
Finally the method can be combined with edge reversal moves to improve the sampler further.
Addendum on the scoring of Gaussian directed acyclic graphical models
We provide a correction to the expression for scoring Gaussian directed acyclic graphical models derived in Geiger and Heckerman [Ann.
Uniform random generation of large acyclic digraphs
Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions.
