Bayesian networks (BNs) are highly effective in handling uncertain problems, which can
assist in decision-making by reasoning with limited and incomplete information. Learning a …

In this paper we introduce a two-step clustering-based strategy, which can automatically
generate prior information from data in order to further improve the accuracy and time …

While there exist many efficient algorithms in the literature for learning Bayesian networks
with discrete random variables, learning when some variables are discrete and others are …

Abstract Structure learning is a fundamental and challenging issue in dealing with Bayesian
networks. In this paper we introduce a two-step clustering-based strategy, which can …

Bayesian network structure learning is one of the current research hotspots in fields such as
statistics and machine learning. Although it has great potential and application prospects …

We study an interesting and challenging problem, learning any part of a Bayesian network
(BN) structure. In this challenge, it will be computationally inefficient using existing global BN …

Abstract 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 …

We develop a novel hybrid method for Bayesian network structure learning called
partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new …

Bayesian networks are probabilistic graphical models widely employed to understand
dependencies in high-dimensional data, and even to facilitate causal discovery. Learning …

We study the problem of learning a Bayesian network (BN) of a set of variables when
structural side information about the system is available. It is well known that learning the …