Latent Dirichlet allocation (LDA) is an important class of hierarchical Bayesian models 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. This toolbox is implemented by MEX C++/MATLAB platform for either Windows or Linux. The current version includes various learning 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 and more BP-based learning algorithms for various LDA-based topic models will be added in the near future. Interested readers may also extend this toolbox for solving more complicated topic modeling problems. The source code is freely available under the GNU General Public Licence, Version 1.0 at http://code.google.com/p/tmbp-topicmodel-beliefpropagation/

本文介绍了一种基于置信传播算法的主题模型工具包（TMBP），该工具包采用MEX C++ / Matlab / Octave实现，可用于Windows 7或Linux操作系统，其中包括学习基于Latent Dirichlet allocation（LDA）的主题模型的BP算法，以及其他几种BP算法，有关源代码的信息请访问给定的链接。

使用置信传播的主题建模工具箱