lda_vem: LDA: Variational EM
In clintpgeorge/ldavem: LDA Variational EM Algorithm

Description Usage Arguments Details Value Note

This implements the variational inference algorithm for the LDA (full Bayesian) model. This includes optimization routines for both α and η hyperparameters.

1
2
3

lda_vem(num_topics, vocab_size, docs_tf, alpha_h, eta_h, vi_max_iter,
  em_max_iter, vi_conv_thresh, em_conv_thresh, estimate_alpha, estimate_eta,
  verbose)

`num_topics`	Number of topics in the corpus
`vocab_size`	Vocabulary size
`docs_tf`	A list of corpus documents read from the Blei corpus using `read_docs` (term indices starts with 0)
`alpha_h`	Hyperparameter for θ sampling
`eta_h`	Smoothing parameter for the β matrix
`vi_max_iter`	Maximum number of iterations for variational inference
`em_max_iter`	Maximum number of iterations for variational EM
`estimate_alpha`	If true, run hyperparameter α optimization
`estimate_eta`	If true, run hyperparameter η optimization
`verbose`	from 0, 1, 2
`vi_conv_threshold`	Convergence threshold for the document variational inference loop
`em_conv_threshold`	Convergence threshold for the variational EM loop