Name
Abstract | ||
|---|---|---|
We review three algorithms for Latent Dirichlet Allocation (LDA). Two of them are variational inference algorithms: Variational Bayesian inference and Online Variational Bayesian inference and one is Markov Chain Monte Carlo (MCMC) algorithm -- Collapsed Gibbs sampling. We compare their time complexity and performance. We find that online variational Bayesian inference is the fastest algorithm and still returns reasonably good results. |
Year | Venue | Field |
|---|---|---|
2013 | arXiv: Learning | Latent Dirichlet allocation,Bayesian inference,Markov chain Monte Carlo,Inference,Algorithm,Artificial intelligence,Bayesian statistics,Time complexity,Mathematics,Gibbs sampling,Machine learning,Variational message passing |
DocType | Volume | Citations |
Journal | abs/1307.0317 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaka Speh | 1 | 13 | 1.05 |
| Andrej Muhic | 2 | 14 | 2.90 |
| Jan Rupnik | 3 | 22 | 5.04 |