lda_cgs_em: LDA: Gibbs-EM
In clintpgeorge/ldamcmc: Markov chain Monte Carlo Algorithms for the Latent Dirichlet Allocation Model
Description
This implements the Gibbs-EM algorithm for LDA that is mentioned in the
paper Topic Modeling: Beyond Bag-of-Words. Wallach (2006).
Usage
1
2
lda_cgs_em(num_topics, vocab_size, docs_tf, alpha_h, eta_h, em_max_iter,
gibbs_max_iter, burn_in, spacing, verbose)
See Also
Other MCMC: lda_acgs_st,
lda_cgs_em_perplexity,
lda_cgs_perplexity,
lda_fgs_BF_perplexity,
lda_fgs_perplexity,
lda_fgs_ppc,
lda_fgs_st_perplexity
clintpgeorge/ldamcmc documentation built on Feb. 22, 2020, 12:39 p.m.
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Description
This implements the Gibbs-EM algorithm for LDA that is mentioned in the paper Topic Modeling: Beyond Bag-of-Words. Wallach (2006).
Usage
1 2 | lda_cgs_em(num_topics, vocab_size, docs_tf, alpha_h, eta_h, em_max_iter,
gibbs_max_iter, burn_in, spacing, verbose)
|
See Also
Other MCMC: lda_acgs_st,
lda_cgs_em_perplexity,
lda_cgs_perplexity,
lda_fgs_BF_perplexity,
lda_fgs_perplexity,
lda_fgs_ppc,
lda_fgs_st_perplexity
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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