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- W3137552275 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 the stochastic block model, this would be the planted partition. The mutual information gauges whether and how well the ground truth can be inferred from the observable data. For a very general model of random factor graphs we verify a formula for the mutual information predicted by physics techniques. As an application we prove a conjecture about low-density generator matrix codes from [Montanari: IEEE Transactions on Information Theory 2005]. Further applications include phase transitions of the stochastic block model and the mixed k-spin model from physics." @default.
- W3137552275 created "2021-03-29" @default.
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- W3137552275 date "2021-01-01" @default.
- W3137552275 modified "2023-09-27" @default.
- W3137552275 title "Inference and Mutual Information on Random Factor Graphs" @default.
- W3137552275 hasPublicationYear "2021" @default.
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