Latent Dirichlet Allocation

Blei et al., 2003, Latent Dirichlet Allocation (Journal of Machine Learning Research)

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5.1 Inference

Why is exact posterior inference in LDA intractable?
The posterior \(p(\theta, \mathbf{z} \mid \mathbf{w}, \alpha, \beta)\) needs the normalizer \(p(\mathbf{w} \mid \alpha, \beta)\), which couples \(\theta\) and \(\beta\) in the sum over latent topics inside the integral. Approximations include Laplace, variational methods and MCMC.
- Blei et al., 2003, Latent Dirichlet Allocation (Journal of Machine Learning Research), §5.1, p. 1003

Source

David M. Blei, Andrew Y. Ng and Michael I. Jordan. 2003. Latent Dirichlet Allocation. In Journal of Machine Learning Research, pp. 993–1022. https://www.jmlr.org/papers/v3/blei03a.html