Probabilistic graphical models such as Bayesian Networks have been increasingly applied to many computer vision problems.Accuracy of inferences in such models depends on the quality of network parameters.The system is distributed freely (under the GNU license) in the spirit of fostering teaching and research.
I have developed graph-based models that represent sets of probability measures over sets of variables; these are often called Fabio G. Still on concepts of independence, I have considered such concepts in the realm of full conditional measures (that is, measures that extend standard probability by adopting conditional probability as the primary object of interest, and hence allowing conditioning on events of probability zero): I have also looked at sequential decision making (that is, planning) under uncertainty.
A summary can be found in: In the process of putting together Java Bayes, I have developed a very general, yet easy to understand, inference algorithm for Bayesian networks.
The method is suited for teaching due to its simplicity.
This paper describes a general framework based on convex optimization to incorporate constraints on parameters with training data to perform Bayesian network parameter estimation.
For complete data, a global optimum solution to maximum likelihood estimation is obtained in polynomial time, while for incomplete data, a modified expectation-maximization method is proposed.