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Bayesian Checking for Topic Models

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Figure

Figure 1: Visualization of variability within topics. Nine randomly selected topics from the New York Times withlow (top row), medium (middle row) and high (bottom row) mutual information between words and documents
Figure 2: News: Observed topic scores (vertical lines)relative to replicated scores, rescaled so that replica-tions have zero mean and unit variance
Figure 3: Groupings decrease MI, but values are still larger than expected. Three ways of grouping words in atopic from the New York Times
Figure 5: News: Events change word distributions.Words with the largest MI from a topic on Iraq’s gov-ernment are shown, with individual scores grouped bymonth.
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