"Latent Dirichlet allocation," D. Blei, A. Ng, and M. Jordan. . Journal of Machine Learning Research, 3:993–1022, January 2003

Latent Dirichlet allocation(LDA) is a generative model that model hidden topics in documents, it is similar to pLSA with an assumption that the topic distribution have a Dirichlet prior. This is basically all I know from the paper and wikipedia. But I don't really know what "Dirichlet prior" is due to my lack of knowledge in statistic. The math in the paper is quite complicate, and I need more time to digest, hopefully I can gain more insight of this paper in the class.
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class notes:
@generate doc:
find N in poisson, observe doc, use dirichlet estimate parameter theta, use parameter to model multinomial to find topic for each word, find a word given topic z and beta.
@jensen's inequality:
log(xa + (1-x)b) >= xlog(a) + (1-x)log(b)