MIT15_097S12_lec15

# The plates are like the for loops in the pseudocode

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

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...
View Full Document

## 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.

Ask a homework question - tutors are online