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Question on running fitlda

I want to run fitlda, with the following specification:
* use Griffiths and Steyvers (2004) Gibbs Sampling algorithm for LDA as they ran it,
* 12 topics (i.e. K=12),
* a symmetric alpha of 50/K (no updating),
* a symmetric beta of .01 (no updating), and
* exactly 2000 iterations (without early termination).
Would that be:
numTopics = 12;
mdl = fitlda(bag,numTopics,'Verbose',1,'InitialTopicConcentration',50,'FitTopicConcentration',false,'WordConcentration',.01,'LogLikelihoodTolerance',0,'IterationLimit',2000);

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R2018b

1 Answer

Answer by Christopher Creutzig on 10 Dec 2018
 Accepted Answer

Gibbs sampling involves stochastic elements (i.e., a pseudorandom number generator), meaning reproducing exactly the results of the 2004 paper will require using their code and their rng settings. (Which is also why in degenerate cases, you do get substantially different answers for multiple fitlda calls.)
Without looking up the definition of β in the original paper, I'm not sure if you want to set 'WordConcentration',.01 or 'WordConcentration',.01*bag.NumWords.
Other than that, the call looks like it should do what you ask, yes.

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Thanks.
For future users trying to do the same thing: Chris's answer helped me figure out that I would need to set β to be:
'WordConcentration',.01*bag.NumWords
The reason that you would want with this alpha and beta is to be consistant with the recommendations in Steyvers and Griffiths (2007).

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