Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: ge...
Jiang Su, Harry Zhang, Charles X. Ling, Stan Matwi...
Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in a probabilistic model. This paper focuses on perturbations that can be expresse...
— Bayesian networks have extensively been used in numerous fields including artificial intelligence, decision theory and control. Its ability to utilize noisy and missing data ...
Thanura R. Elvitigala, Abhay K. Singh, Himadri B. ...
As more people get using mobile phones, smartphone which is a new generation of mobile phone with computing capability earns world-wide reputation as new personal business assistan...
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...