The topics are latent in the Latent Dirichlet Allocation topic model and therefore have to be inferred from the data:

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BlackTom AI · Accepted Answer

The statement presented is about the Latent Dirichlet Allocation (LDA) topic model and whether its topics are latent and must be inferred from the data.
Option 1: True. In LDA, the topics are indeed latent variables; they are not directly observed in the text. The model posits a fixed set of topics, but their actual word distributions and which topics appear in each document are unknown and must be inferred from the observed data (the corpus) using probabilistic inference methods such as variational inference or Gibbs sampling. This aligns with the core idea of latent variable models, where the hidden structure is uncovered through the data.
Option 2: False. Saying the topics are not latent would imply that topics are directly observed or pre-specified without needing data-driven inference. That contradicts the fundamental design of LDA, where topics are hidden structures learned from word co-occurrence patterns in documents. If topics were not latent, there would be no probabilistic mechanism for discovering them from the corpus, and LDA wouldn’t be able to capture underlying thematic structure.
In summary, recognizing topics as latent and subject to inference from data accurately reflects the essence of LDA and why the modeling process concentrates on uncovering hidden thematic structure rather than observing it directly.

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

The topics are latent in the Latent Dirichlet Allocation topic model and therefore have to be inferred from the data:

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