The plates are like the for loops in the pseudocode

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Unformatted text preview: distribution over all possible words. For example, given the topic is “Sports”, the probability of having the word “football” might be high; if the topic were “Weather”, the probability of hav­ ing the word “football” might be lower. Other words, like “the” will have a high probability regardless of the topic. If words are chosen from a set of W possible words, then we let φk ∈ JW be the multinomial parameter over words for topic k . Word j of document i, denoted wi,j , will be generated by the dis­ tribution over words corresponding to the topic zi,j : wi,j ∼ Multinomial(φzi,j ). Finally, we give prior distributions for the parameters θi and φk . The multi­ nomial distribution is a generalization of the binomial distribution, and its 20 conjugate prior is a generalization of the beta distribution: the Dirichlet distribution. Thus we model the data with the following generative model: 1. For document i = 1, . . . , m, choose the document’s topic distribution θi ∼ Dirichlet(α), where α ∈ JK is the prior hyperparameter. 2. For topic k = 1, . . . , K , choose the topic’s word distribution φk ∼ Di...
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This note was uploaded on 03/24/2014 for the course MIT 15.097 taught by Professor Cynthiarudin during the Spring '12 term at MIT.

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