mApplyBern

the Bernoulli probability vector (along events)

prob

Calculate Bernoulli likelihood row-wise for binary events

An implementation of Multi-View Clustering (Bickel and Scheffer, 2004). Documents are generated by drawing word values from a categorical distribution for each word, given the cluster. This means words are not counted (multinomial, as in the paper), but words take on different values from a finite set of values (categorical). Thus, it implements Mixture of Categoricals EM (as opposed to Mixture of Multinomials developed in the paper), and Spherical k-Means. The latter represents documents as vectors in the categorical space.

mApplyBern: Calculate Bernoulli likelihood row-wise for binary events...

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