Minimum Discrepancy Estimation in Probabilistic Knowledge Structures

Practical applications of the theory of knowledge structures often rely on a probabilistic version, known as the basic local independence model. The paper outlines various procedures for estimating its parameters, including maximum likelihood (ML) via the expectation-maximization (EM) algorithm, the computationally efficient minimum discrepancy (MD) estimation as well as MDML, a hybrid method combining the two approaches.