  Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model
for probabilistic topic modeling, which attracts worldwide interests and
touches on many important applications in text mining, computer vision and
computational biology. This paper introduces a topic modeling toolbox (TMBP)
based on the belief propagation (BP) algorithms. TMBP toolbox is implemented by
MEX C++/Matlab/Octave for either Windows 7 or Linux. Compared with existing
topic modeling packages, the novelty of this toolbox lies in the BP algorithms
for learning LDA-based topic models. The current version includes BP algorithms
for latent Dirichlet allocation (LDA), author-topic models (ATM), relational
topic models (RTM), and labeled LDA (LaLDA). This toolbox is an ongoing project
and more BP-based algorithms for various topic models will be added in the near
future. Interested users may also extend BP algorithms for learning more
complicated topic models. The source codes are freely available under the GNU
General Public Licence, Version 1.0 at https://mloss.org/software/view/399/.
