Advanced algorithms of bayesian network learning and inference from inconsistent prior knowledge and sparse data with applications in computational biology and computer vision

Bayesian networks are a popular class of graphical probabilistic models for researches and applications in the field of Artificial Intelligence. Bayesian network are built on Bayes’ theorem (16) and allow to represent a joint probability distribution over a set of variables in the network. In Bayesian probabilistic inference, the joint distribution over the set of variables in a Bayesian network can be used to calculate the probabilities of any configuration of these variables given fixed values of another set of variables, called observations or evidence. Bayesian networks have been widely used for efficient probabilistic inference and data mining in many fields, such as computational biology and computer vision (17; 18).