The output of the Latent Dirichlet Allocation (LDA) topic model is:

Question

BlackTom AI · Accepted Answer

When considering what the Latent Dirichlet Allocation (LDA) model outputs, it's helpful to recall the two key probabilistic components involved in LDA.
Option 1: 'The distribution of topics in a document, for each document in a corpus'—this describes the document-topic distribution (often denoted theta or similar), which tells you, for every document, how much of each topic is present. This is a fundamental part of LDA's output.
Option 2: 'The distribution of words for each topic found in the corpus'—this captures the topic-word distribution (often denoted phi), i.e., for each topic, the probability of each word under that topic. This is another essential component the model estimates.
Option 3: 'Both A and B'—since LDA jointly estimates both the per-document topic distributions and the per-topic word distributions, this option correctly summarizes the model's outputs.
To evaluate the options critically, note that LDA does not merely give one of these components in isolation; it provides both: how topics are distributed across documents and how words are distributed within topics. A standalone statement focusing only on the document-level distribution ignores the learned topic-word associations, and a standalone statement focusing only on the topic-word distributions ignores the document-level mixture aspect.
Thus, considering the complete scope of LDA's outputs, Option 1 covers one part, Option 2 covers another, and Option 3 integrates both aspects, reflecting the full structure that LDA yields for a corpus.

SP2025.B69.DAT.562.24 COMPLETE Quiz #4 AFTER VIDEO 3: Latent Dirichlet Allocation (LDA)

The output of the Latent Dirichlet Allocation (LDA) topic model is:

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