博文
Gibbs Sampling Algorithm for LDA
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最近利用Gibbs Sampling算法对许多主题模型（topic model）进行了推导
https://wap.sciencenet.cn/blog-611051-566845.html
上一篇：下一代测序中ChIP-seq数据的处理与分析
下一篇：Gibbs Sampling Algorithm for AT Model
目前考虑的主题模型包括：LDA (Latent Dirichlet Allocation)，AT (Author-Topic)，ACT (Author-Conference-Topic)，ToT (Topic over Time)，TNG (Topic N-Gram)，BTM (Bigram Topic Model), LDACOL (LDA Collocation)等
最近会不断的贴在此处，供大家批评指正
今天先贴第一篇：LDAGibbs（全文见LDAGibbs.pdf），主要参考以下文献：
@TECHREPORT{Hei09,
author = {Heinrich, Gregor},
title = {Parameter Estimation for Text Analysis},
institution = {vsonix GmbH and University of Leipzig},
year = {2009},
type = {Technical Report Version 2.9},
abstract = {Presents parameter estimation methods common with discrete probability
distributions, which is of particular interest in text modeling.
Starting with maximum likelihood, a posteriori and Bayesian estimation,
central concepts like conjugate distributions and Bayesian networks
are reviewed. As an application, the model of latent Dirichlet allocation
(LDA) is explained in detail with a full derivation of an aaproximate
inference algorithm based on Gibbs sampling, including a discussion
of Dirichlet hyperparameter estimation.},
}
https://wap.sciencenet.cn/blog-611051-566845.html
上一篇：下一代测序中ChIP-seq数据的处理与分析
下一篇：Gibbs Sampling Algorithm for AT Model
