Inference and mutual information on random factor graphs

Max Hahn-Klimroth
Max Hahn-Klimroth
Philipp Loick
Philipp Loick
Noela Müller
Noela Müller
Matija Pasch
Matija Pasch
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Abstract:

Random factor graphs provide a powerful framework for the study of inference problems such as decoding problems or the stochastic block model. Information-theoretically the key quantity of interest is the mutual information between the observed factor graph and the underlying ground truth around which the factor graph was created; in th...More

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