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. 2013 Jun 24;8(6):e67410. doi: 10.1371/journal.pone.0067410

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© 2013 Praveen, Fröhlich

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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(a) A general Latent Factor Model (LFM). The random variables , and are highly related variables (left) and an assumption that these related random variables originate from a common, true but unknown variable results a bayesian network (right) in case of networks is the true but unknown network. (b) A generalized view of a Noisy-OR model showing the relation between causes and effect through a Noisy-OR function.

Inline graphic — (a) A general Latent Factor Model (LFM). The random variables , and are highly related variables (left) and an assumption that these related random variables originate from a common, true but unknown variable results a bayesian network (right) in case of networks is the true but unknown network. (b) A generalized view of a Noisy-OR model showing the relation between causes and effect through a Noisy-OR function.