ldamcmc: Markov chain Monte Carlo Algorithms for the Latent Dirichlet...

In clintpgeorge/ldamcmc: Markov chain Monte Carlo Algorithms for the Latent Dirichlet Allocation Model

Description

This R package implements several Markov chain Monte Carlo (MCMC) algorithms for the latent Dirichlet allocation (LDA, Blei et al. 2003) model. This includes:

1. The augmented collapsed Gibbs sampling (ACGS, Griffiths and Steyvers 2004, George and Doss 2015) algorithm

2. The full Gibbs sampling (FGS, George and Doss 2015) algorithm

3. The serial tempering (George and Doss 2015, Geyer 2011) algorithm

4. Hyperparameter selection in the LDA model (George and Doss 2015)

5. Posterior predictive checking (PPC, Chen and Doss 2015)

Author(s)

Clint P. George and Hani Doss

References

1. Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research 3 993-1022.

2. Chen, Z. and Doss, H. (2015). Inference for the number of topics in the latent Dirichlet allocation model via Bayesian mixture modelling. Tech. rep., Department of Statistics, University of Florida.

3. George, C.P. and Doss, H. (2015). Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model. Tech. rep., Department of Computer and Information Science and Engineering, University of Florida

4. Geyer, C. J. (2011). Importance sampling, simulated tempering, and umbrella sampling. In Handbook of Markov Chain Monte Carlo (S. P. Brooks, A. E. Gelman, G. L. Jones and X. L. Meng, eds.). Chapman & Hall/CRC, Boca Raton, 295-311.

5. Griffiths, T. L. and Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences 101 5228-5235.

clintpgeorge/ldamcmc documentation built on Feb. 22, 2020, 12:39 p.m.