After trying to understand Blei's 2009 paper "Topic Models" and reading several websites. This blog entry is the simplest explanation I could find. I still don't understand how LDA works.
According to the blog entry mentioned previously LDA consists of two steps:
- The generative process
- The learning (when a new set of documents arrives and we wish to discover its topical compositions)
The blog mentions that in this learning process we start with random assignments and then we compute: p(topic t | document d) and p(word w | topic ) and reassign the word to a new topic, where we choose topic t with probability p(topic t | document d) * p(word w | topic t) .
I don't see where in this learning process we use the "output" of the generative process and what exactly is this "output".