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Developing the next generation of robust Bayesian networks: theory and efficient inference algorithms for mixed credal networks (3E004715)

Credal networks are Bayesian networks with imprecise (interval-valued) local probabilities, thereby allowing for robust inferences. This project develops a new type of credal networks, called mixed credal networks. The advantage of this new type is that they do not suffer from the typical computational problems that occur for other types, which allows us to develop efficient inference algorithms.

Date:1 Oct 2015  →  30 Sep 2017
Keywords:algorithms, credal networks, imprecise probability
Disciplines:Programming languages, Applied mathematics in specific fields, Scientific computing, Other information and computing sciences, Cognitive science and intelligent systems, Visual computing, Artificial intelligence, Statistics and numerical methods, Information systems, Information sciences, Computer architecture and networks, Distributed computing, Theoretical computer science