logLik,TopicModel-method

logLik,Gibbs_list-method

methods

logLik-methods

Provides an interface to the C code for Latent Dirichlet
Allocation (LDA) models and Correlated Topics Models
(CTM) by David M. Blei and co-authors and the C++ code
for fitting LDA models using Gibbs sampling by Xuan-Hieu
Phan and co-authors.

Bettina Gruen

topicmodels

Topic Models

Bettina Grün

Kurt Hornik

David M Blei

John D Lafferty

Xuan-Hieu Phan

Makoto Matsumoto

Takuji Nishimura

Shawn Cokus

logLik-methods function

<dl>
 <dt>object = TopicModel:</dt>
<dd>Compute the log-likelihood of a
 <code>"TopicModel"</code> object. For <code>"VEM"</code> objects the sum of
 the log-likelihood of all documents given the parameters for the
 topic distribution and for the word distribution of each topic is
 approximated using the variational parameters and underestimates
 the log-likelihood by the Kullback-Leibler divergence between the
 variational posterior probability and the true posterior
 probability.</dd> <dt>object = Gibbs_list:</dt>
<dd>Compute the log-likelihoods of the
 <code>"TopicModel"</code> objects contained in the <code>"Gibbs_list"</code>
 object.</dd> 
</dl>

Methods

Methods for Function logLik — logLik-methods

<dl>
 <dt>object = TopicModel:</dt>
<dd>Compute the log-likelihood of a
 <code>"TopicModel"</code> object. For <code>"VEM"</code> objects the sum of
 the log-likelihood of all documents given the parameters for the
 topic distribution and for the word distribution of each topic is
 approximated using the variational parameters and underestimates
 the log-likelihood by the Kullback-Leibler divergence between the
 variational posterior probability and the true posterior
 probability.</dd>

 <dt>object = Gibbs_list:</dt>
<dd>Compute the log-likelihoods of the
 <code>"TopicModel"</code> objects contained in the <code>"Gibbs_list"</code>
 object.</dd>

 
</dl>

Methods for Function logLik

logLik-methods: Methods for Function logLik

Description

Arguments

Methods