Rear-end crash is one of the most common types of traffic crashes in the US A good
understanding of its characteristics and contributing factors is of practical importance …

A classic approach for learning Bayesian networks from data is to identify a maximum a
posteriori (MAP) network structure. In the case of discrete Bayesian networks, MAP networks …

Environmental conflicts have been a top global focus and issue for human's sustainable
development. China is confronted with a serious situation with a rigid demand of ecological …

Bowing to the burgeoning needs of online consumers, exploitation of social media content
for extrapolating buyer-centric information is gaining increasing attention of researchers and …

Learning progressions (LPs) serve as frameworks for understanding the level of complexity
of students' ideas within a domain. Multifaceted LPs contain multiple interrelated constructs …

Several structure learning algorithms have been proposed towards discovering causal or
Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by …

Datasets with multiple variables are useful to identify trends promptly that can be used to
support planning and decision making. However, it is quite common for these datasets to …

The basic idea of the geometric approach to learning a Bayesian network (BN) structure is to
represent every BN structure by a certain vector. If the vector representative is chosen …

This paper considers the problem of learning multinomial distributions from a sample of
independent observations. The Bayesian approach usually assumes a prior Dirichlet …

This work presents two new score functions based on the Bayesian Dirichlet equivalent
uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity …