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Found 7 papers, 2 papers with code
Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning
This paper describes an approximate algorithm that performs parallel sampling on Candidate Parent Sets (CPSs), and can be viewed as an extension of MINOBS which is a state-of-the-art algorithm for structure learning from high dimensional data.
Effective and efficient structure learning with pruning and model averaging strategies
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function.
A survey of Bayesian Network structure learning
This paper provides a comprehensive review of combinatoric algorithms proposed for learning BN structure from data, describing 74 algorithms including prototypical, well-established and state-of-the-art approaches.
The impact of prior knowledge on causal structure learning
Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data.
Improving Bayesian Network Structure Learning in the Presence of Measurement Error
Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables.
Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets
The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data
This paper investigates the performance of 15 structure learning algorithms.
