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I'm reading the Latent Dirichlet Allocation paper trying to understand it. However I got stuck at the very first part! When they sampled from a multinomial distribution and considered the result to be a single number! My understanding if the multinomial distribution is to return a vector where the number of its elements are the same as the number of probability vector parameter plugged into the multinomial.

I'm not sure if I misunderstood the notation.

Here is the link to the paper $\text{(page $4$)}$: http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf

$\mathbf 3$. Latent Dirichlet allocation

Latent Dirichlet allocation (LDA) is a generative probabilistic model of a corpus. The basic idea is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words.$^1$
$\quad$LDA assume the following generative process for each document $\mathbf w$ in a corpus $D$:

  1. Choose $N\sim\operatorname{Poisson}(\xi)$.

  2. Choose $\theta\sim\operatorname{Dir}(\alpha).$

  3. For each of the $N$ words $w_n$:

    • $\text a.$ Choose a topic $z_n\sim\operatorname{Multinomial}(\theta)$.
    • $\text b.$ Choose a word $w_n$ from $p(w_n|z_n,\beta)$, a multinomial probability conditioned on the topic $z_n$.
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A multinomial distribution is a probability distribution on a vector-valued random variable.

That said, from what I can tell from the paper, "words" and "topics" are vectors, not scalars. A "word" consists of a $(0,1)$-valued vector. The length of the vector is the size of the set of all words. While it is not explicitly described in the paper, a "topic" is implied to be a vector, whose length is the same as that for words. I think a topic can be regarded as a collection of words; i.e., it is a vector for which more than one position can be equal to $1$ (unlike a word, for which exactly one position is $1$ and the others are all $0$).

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  • $\begingroup$ I don't really understand it. A multinomial distribution accepts a vector of scalars (let's say [3, 1, 5], where each scalar is the number of successes of a category) and returns the probability that this combination of successes happen. So if I sample from such a distribution I should get a vector [k1,k2,k3]. I am true? $\endgroup$ – Jack Twain Jan 4 '14 at 14:46
  • $\begingroup$ What I am sure about is that Z is a single scalar and theta is a real-valued vector. $\endgroup$ – Jack Twain Jan 4 '14 at 14:47

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