# [Topic-models] LDA paper by Blei - some more questions

Veena T veenat2005 at gmail.com
Sun Nov 23 01:27:14 EST 2008

>Hi list readers,

>The paper mentions the following step:

>The problematic coupling between \theta and \beta arises due to the
>edges between \theta, z, and w. By dropping these edges and the w
>nodes, and endowing the resulting simpli?ed graphical model with free
>variational parameters, we obtain a family of distributions on the
>latent variables.

>As I am not a native english speaker can someone please tell me what
>"endowing" means in this context? Also, why is it possible to remove
>the edges and nodes, and create some completely different model, yet
>the one approximating things the way we want? I expect this has
>something to do with the type of distribution ?
Even I have similar doubts. By doing so, we are infact altering the model
altogether. However, the variational inference theory tells like that. I am
also searching a justification for that.

>The other question is related to EM algorithm. It was already pointed
>to me that parameter estimation should be done first, and then the
>inference, but the paper not only explains these in reversed order,
>but the EM algorithm outline is specified as follows:

>1. (E-step) For each document, ?nd the optimizing values of the
>variational parameters
>This is done as described in the previous section.

>2. (M-step) Maximize the resulting lower bound on the log likelihood
>with respect to the model parameters \alpha and \beta. This
>corresponds to ?nding maximum likelihood estimates with expected
>suf?cient statistics for each document under the approximate posterior
>which is computed in the E-step.

>So the first step of the algorithm explicitly relies on the inference
>phase being completed as the variational parameters are valid
>(optimized) only after inference phase (?).

See, here I have a doubt as to your undertsadning of the word 'inference'. I
feel that you are referring to the word in the context of infering some
results from the model for the unseen examples. Is that true? What ever the
'inference', paper mentios is part of model building it self.

>I'm kinda stuck on this one.

>I'd appreciate any hints in the right direction.

--
Veena Srinivas,
PhD scholar,
Speech and Vision Lab