clda: Algorithms for the Compound Latent Dirichlet Allocation Model
In clintpgeorge/clda: Approximate Inference Algorithms for the Compound Latent Dirichlet Allocation Model
Description
This R package implements the following inference algorithms
for the compound latent Dirichlet allocation (CLDA) model.
Auxiliary variable update within Gibbs sampler (AGS)
Metropolis adjusted Langevin algorithm within Gibbs sampler (MGS)
Variational Expectation Maximization (VEM)
Author(s)
Clint P. George, Wei Xia, and George Michailidis
clintpgeorge/clda documentation built on May 13, 2019, 8 p.m.
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Description
This R package implements the following inference algorithms for the compound latent Dirichlet allocation (CLDA) model.
Auxiliary variable update within Gibbs sampler (AGS)
Metropolis adjusted Langevin algorithm within Gibbs sampler (MGS)
Variational Expectation Maximization (VEM)
Author(s)
Clint P. George, Wei Xia, and George Michailidis
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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