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Now, given a set of items with their associated words as the observed data,


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Alpha represents document-topic density - with a higher alpha, documents are made up of more topics, and with lower alpha, documents contain fewer topics.
Beta represents topic-word density - with a high beta, topics are made up of most of the words in the corpus, and with a low beta they consist of few words.

higher alpha results in a more specific topic distribution per document. Likewise, beta results in a more specific word distribution per topic.