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[D] Why the Dirichlet as a prior for LDA?

It seems the Dirichlet distribution is pretty ubiquitous in its use as a prior for LDA and other types of topic models. However, in variational inference the Dirichlet doesn’t have a closed form reparameterization. What are the various properties that make the Dirichlet so successful, and are there any other distributions or methods that exhibit these properties without having to use the Dirichlet?

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.