8 citations · 10 across the 4 of their papers we have counts for
7 papers
Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning
Zhigao Guo, Anthony C. Constantinou
Score-based algorithms that learn the structure of Bayesian networks can be used for both exact and approximate solutions. While approximate learning scales better with the number…
Effective and efficient structure learning with pruning and model averaging strategies
Anthony C. Constantinou, Yang Liu, Neville K. Kitson +2
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 gi…
A survey of Bayesian Network structure learning
Neville K. Kitson, Anthony C. Constantinou, Zhigao Guo +2
Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology…
The impact of prior knowledge on causal structure learning
Anthony C. Constantinou, Zhigao Guo, Neville K. Kitson
Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions t…
Improving Bayesian Network Structure Learning in the Presence of Measurement Error
Yang Liu, Anthony C. Constantinou, ZhiGao Guo
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 var…
Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets
Zhigao Guo, Anthony C. Constantinou
Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions…