[Topic-models] LDA beginner's questions
Mateusz Berezecki
mateuszb at gmail.com
Sun Nov 2 13:28:09 EST 2008
Hi topic list,
I am a undergrad student and a newcomer to the LDA and this list as
well. I'd like to ask a lot of questions which I guess are of a very
basic nature to you, guys, but to me most of this stuff is really
unknown so I am tackling multiple new things/techniques/ideas all at
once.
After reading LDA paper (Blei et al 2003) a couple of times my grasp
of the concepts is somewhat better but I still need some
clarifications.
1. \alpha parameters are not known and are used only conceptually in
the generative process. They are to be estimated ?
2. \beta parameters (word probabilities, i.e. p(w^j = 1 | z^i = 1) are
not known in advance and are to be estimated ? \beta is in fact a
matrix of conditional probabilities?
3. Whenever we choose \theta from the Dirichlet(\alpha) distribution
we effectively get all but one (as N is assumed to be fixed)
parameters for the Multinomial distribution used later on ?
4. Why is p(z_n | \theta) given by \theta_i for the unique i such that
z_n^i = 1 ?
5. What is the definition of a variational parameter? What are they?
(I'm completely new to variational methods)
6. What is the high level overview of the LDA algorithm?
What steps do we make in order to make the LDA work correctly?
Estimate parameters and then do inference, or the other way around? I
think this is missing in the paper.
7. Is the LDA-C a 1-1 implementation of what is published in the
paper? I was trying to read the code but for the first few passes over
the code I don't see any direct mapping to most of the equations
published in the paper.
I'd appreciate even the most concise answers (but not too much as this
can result in another question to the answer ;) ).
best regards,
Mateusz Berezecki
More information about the Topic-models
mailing list