Abstract Structure learning is a very important problem in the field of Bayesian networks
(BNs). It is also an active research area for more than 2 decades; therefore, many …

Bayesian networks are analytical models that may represent probabilistic dependent
connections among variables and are useful in machine learning for generating knowledge …

Bayesian networks are useful analytical models for designing the structure of knowledge in
machine learning which can represent probabilistic dependency relationships among the …

Las diferentes disciplinas ingenieriles avanzan cada año en cuanto a desarrollos y
descubrimientos que, con el soporte de la ciencia, le brindan mejores alternativas de vida a …

Bayesian networks are useful analytical models for designing the structure of knowledge in
machine learning. Probabilistic dependency relationships among the variables can be …

Bayesian networks are useful analytical models for designing the structure of knowledge in
machine learning. Bayesian networks can represent probabilistic dependency relationships …

The project success is critical to the business performance in the era of fierce competition
and globalization. The basis for project success lies in the capabilities of managing risks …

Bayesian networks structure learning is known to be computationally challenging,
essentially in order to the quantity of applicant graphs is super exponential in the proportion …

In machine-learning, some of the helpful scientific models during the production of a
structure of knowledge are Bayesian networks. They can draw the relationships of …

ABSTRACT A considerable amount of energy is consumed with every completed ship
voyage and each port operation performed. Recent regulative approaches by the IMO are to …