时间总是不够用，这里就不自己写了，摘自一篇转发的博客，感觉挺有用！
一个大牛写的介绍，貌似需FQ
David M.Blei主页：http://www.cs.princeton.edu/~blei/publications.html，上面有布雷最新的文章：Introduction to probabilistic topic models
以下内容来自网络，但是作者已经不可考啦，抱歉没法找到原始引用
关于LDA并行化：
那么若利用MapReduce实现，怎样的近似方法好呢？
斯坦福的ScalaNLP项目值得一看：
http://nlp.stanford.edu/javanlp/scala/scaladoc/scalanlp/cluster/DistributedGibbsLDA$object.html
另外还有NIPS2007的论文：
Distributed Inference for Latent DirichletAllocation http://books.nips.cc/papers/files/nips20/NIPS2007_0672
ICML2008的论文：
Fully Distributed EM for Very Large Datasetshttp://www.cs.berkeley.edu/~jawolfe/pubs/08-icml-em
LDA和HLDA：
（1）D. M. Blei, et al., "Latent Dirichlet allocation," Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
（2）T. L. Griffiths and M. Steyvers, "Finding scientific topics," Proceedings of the National Academy of Sciences, vol. 101, pp. 5228-5235, 2004.
（3）D. M. Blei, et al., "Hierarchical Topic Models and the Nested Chinese Restaurant Process," NIPS, 2003.
（4）Blei的LDA视频教程：http://videolectures.net/mlss09uk_blei_tm/
（5）Teh的关于Dirichlet Processes的视频教程：http://videolectures.net/mlss07_teh_dp/
（6）Blei的毕业论文：http://www.cs.princeton.edu/~blei/papers/Blei2004.pdf
（7）Jordan的报告：http://www.icms.org.uk/downloads/mixtures/jordan_talk.pdf
（8）G. Heinrich, "Parameter Estimation for Text Analysis," http://www.arbylon.net/publications/text-est.pdf
基础知识：
（1）P. Johnson and M. Beverlin, “Beta Distribution,” http://pj.freefaculty.org/ps707/Distributions/Beta.pdf
（2）M. Beverlin and P. Johnson, “The Dirichlet Family,” http://pj.freefaculty.org/stat/Distributions/Dirichlet.pdf
（3）P. Johnson, “Conjugate Prior and Mixture Distributions”, http://pj.freefaculty.org/stat/TimeSeries/ConjugateDistributions.pdf
（4）P.J. Green, “Colouring and Breaking Sticks:Random Distributions and Heterogeneous Clustering”, http://www.maths.bris.ac.uk/~mapjg/papers/GreenCDP.pdf
（5）Y. W. Teh, "Dirichlet Process", http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdf
（6）Y. W. Teh and M. I. Jordan, "Hierarchical Bayesian Nonparametric Models with Applications,”
http://www.stat.berkeley.edu/tech-reports/770.pdf
（7）T. P. Minka, "Estimating a Dirichlet Distribution", http://research.microsoft.com/en-us/um/people/minka/papers/dirichlet/minka-dirichlet.pdf
（8）北邮论坛的LDA导读：[导读]文本处理、图像标注中的一篇重要论文Latent Dirichlet Allocation，http://bbs.byr.edu.cn/article/PR_AI/2530?p=1
（9）Zhou Li的LDA Note：http://lsa-lda.googlecode.com/files/Latent Dirichlet Allocation note.pdf
（10）C. M. Bishop, “Pattern Recognition And Machine Learning,” Springer, 2006.
代码：
（1）Blei的LDA代码（C）：http://www.cs.princeton.edu/~blei/lda-c/index.html
（2）BLei的HLDA代码（C）：http://www.cs.princeton.edu/~blei/downloads/hlda-c.tgz
（3）Gibbs LDA（C++）：http://gibbslda.sourceforge.net/
（4）Delta LDA（Python）：http://pages.cs.wisc.edu/~andrzeje/research/deltaLDA.tgz
（5）Griffiths和Steyvers的Topic Modeling工具箱：http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm
（6）LDA（Java）：http://www.arbylon.net/projects/
（7）Mochihashi的LDA（C，Matlab）：http://chasen.org/~daiti-m/dist/lda/
（8）Chua的LDA（C#）：http://www.mysmu.edu/phdis2009/freddy.chua.2009/programs/lda.zip
（9）Chua的HLDA（C#）：http://www.mysmu.edu/phdis2009/freddy.chua.2009/programs/hlda.zip
其他：
（1）S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PAMI-6, pp. 721-741, 1984.
（2）B. C. Russell, et al., "Using Multiple Segmentations to Discover Objects and their Extent in Image Collections," in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 2006, pp. 1605-1614.
（3）J. Sivic, et al., "Discovering objects and their location in images," in Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, 2005, pp. 370-377 Vol. 1.
（4）F. C. T. Chua, "Summarizing Amazon Reviews using Hierarchical Clustering," http://www.mysmu.edu/phdis2009/freddy.chua.2009/papers/amazon.pdf
（5）F. C. T. Chua, "Dimensionality Reduction and Clustering of Text Documents,” http://www.mysmu.edu/phdis2009/freddy.chua.2009/papers/probabilisticIR.pdf
（6）D Bacciu, "Probabilistic Generative Models for Machine Vision," http://www.math.unipd.it/~sperduti/AI09/bacciu_unipd_handouts.pdf