Automated Topic Analysis of the Voynich Manuscript

[quimqu]

So you are trying several K-clustering algorithms on the same set of data points, where each point is a bag-of-words -- essentially an N-vector where N is the number of distinct word types.  Where the algorithms do not assign each parag to a single cluster (topic), but gives it a "belonging" or "mixing" score for each cluster.  Is that correct?

Can we interpret those scores as Bayesian probabilities of the parag belonging to each topic?

Almost, but not quite. Yes: each paragraph is turned into a bag-of-words vector, and I try several values of K.

But it isn’t classic clustering where each paragraph goes in one bucket. It’s a mixed-membership topic model: the model learns K word-themes, and each paragraph is a mixture of those themes (different amounts for each). With DMR, the expected mix can also depend on metadata (language A/B, writing hand, Currier), while the themes themselves (their word lists) stay shared.

For LDA/DMR, you can read the scores as “what fraction of this paragraph’s tokens the model thinks come from each theme.” They sum to 1 and are a reasonable probabilistic mix over themes—more like “probability a random token in this paragraph was generated by topic k,” not “probability the whole paragraph belongs to topic k” (documents are assumed to mix topics). They’re approximate posteriors from the model.

For NMF, the numbers are non-negative weights, not probabilities. You can normalize them to add up to 1 for plotting, but that makes them proportions by construction, not Bayesian probabilities.