Imprecise (Full Conditional) Probabilities, Graphs and Graphoids Independence Assumptions - a podcast by MCMP Team

Fabio G. Cozman (Sao Paulo) gives a talk at the Workshop on Imprecise Probabilities in Statistics and Philosophy (27-28 June, 2014) titled "Imprecise (Full Conditional) Probabilities, Graphs and Graphoids Independence Assumptions". Abstract: Research in artificial intelligence systems has often employed graphs to encode multivariate probability distributions. Such graph-theoretical formalisms heavily employ independence assumptions so as to simplify model construction and manipulation. Another line of research has focused on the combination of logical and probabilistic formalisms for knowledge representation, often without any explicit discussion of independence assumptions. In this talk we examine (1) graph-theoretical models, called credal networks, that represent sets of probability distributions and various independence assumptions; and (2) languages that combine logical constructs with graph-theoretical models, so as to provide tractability and exibility. The challenges in combining these various formalisms are discussed, together with insights on how to make them work together.

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