Difference between revisions of "Midterm Report Nitin Yandong Ming Yanbo"

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(Created page with '== Team members == Nitin Agarwal Yandong Liu Yanbo Xu Ming Sun == LDA results == == ATM results == == Gibbs Sampling for Co…')
 
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<math>P(Z,X,R|W) = \frac{P(Z,X,R,W)}{\sum_{Z,X,R} P(Z,X,R,W)}</math>
 
<math>P(Z,X,R|W) = \frac{P(Z,X,R,W)}{\sum_{Z,X,R} P(Z,X,R,W)}</math>
  
We begin by calculating <math>P(W|Z,X,R)</math>:
+
We begin by calculating <math>P(W|Z,X,R)</math> and <math>P(Z,X,R)</math>:
 +
 
  
 
<math>P(W|Z,X,R) = P(W|Z) = \prod_{z = 1}^{T} (\frac{\Gamma (\sum_{v = 1}^{V} \beta_{v})}{\prod_{v=1}^{V} \Gamma (\beta_v)} ( \frac{\prod_{v=1}^{V} \Gamma (n_{z}^{w_v} + \beta_v)}{\Gamma (\sum_{v=1}^{V} \beta_v + \sum_{v=1}^{V} n_{z}^{w_v})}))</math>
 
<math>P(W|Z,X,R) = P(W|Z) = \prod_{z = 1}^{T} (\frac{\Gamma (\sum_{v = 1}^{V} \beta_{v})}{\prod_{v=1}^{V} \Gamma (\beta_v)} ( \frac{\prod_{v=1}^{V} \Gamma (n_{z}^{w_v} + \beta_v)}{\Gamma (\sum_{v=1}^{V} \beta_v + \sum_{v=1}^{V} n_{z}^{w_v})}))</math>
  
then,
 
  
<math>P(Z,X,R) = ()()</math>
+
<math>P(Z,X,R) = (\prod_{i_w = 1}^{W} \frac{1}{n_{r_{i_w}} (a_{i_w}) + \eta_{r_{i_w}}}) \prod_{p=1}^{P} (\frac{\Gamma (\sum_z \alpha_z)}{\prod_{z=1}^{T} \Gamma (\alpha_z)} \frac{\prod_z \Gamma (n_p^z + \alpha_z)}{\Gamma (\sum_z \alpha_z + \sum_z n_p^z)})</math>,
 +
 
 +
where P is the number of all the different author-collaborator-favor of collaboration combination (a,a',r).
 +
 
 +
So the Gibbs sampling of <math>P(z_i, x_i, r_i, w_i | Z_{-i}, X_{-i}, R_{-i}, W_{-i})</math> :
 +
 
 +
 
 +
<math>P(z_i, x_i, r_i, w_i | Z_{-i}, X_{-i}, R_{-i}, W_{-i})</math>
 +
 
 +
<math>= \frac{P(Z,X,R,W)}{P(Z_{-i}, X_{-i}, R_{-i}, W_{-i})}</math>
 +
 
 +
<math>= \frac{1}{n_{r_i} + \eta_{r_i}} \frac{n_{p,-i}^{t} + \alpha_t}{\sum_z n_{p,-i}^z + \sum_z \alpha_z} \frac{n_{t,-i}^{w_v} + \beta_v}{\sum_v n_{t,-i} + \sum_v \beta_v}</math>
 +
 
 +
 
 +
Further manipulation can turn the above equation into update equations for the topic and author-collaboration of each corpus token:
 +
 
 +
 
 +
<math>P(z_i | Z_{-i}, X, W,R) \propto \frac{n_{z_i}^{w_v} + \beta_v}{\sum_v n_{z_i}^{w_v} + \beta_v} \frac{n_{x_i}^{z_i} + \alpha_{z_i}}{\sum_{z'} n_{x_i}^{z'} + \alpha_{z'}} \frac{n_{r_i} + \eta_{r_i}}{\sum_{r_i} (n_{r_i} + \eta_{r_i})}</math>
 +
 
 +
 
 +
<math>P(x_i,r_i | Z,X_{-i},W,R_{-i}) \propto \frac{n_{x_i, r_i}^{z_i} +\alpha_{z_i}}{\sum_{z'} n_{x_i,r_i}^{z'} + \alpha_{z'}} \frac{n_{r_i} + \eta_{r_i}}{\sum_{r_i} (n_{r_i} + \eta_{r_i})}</math>

Revision as of 23:31, 16 March 2011

Team members

Nitin Agarwal

Yandong Liu

Yanbo Xu

Ming Sun

LDA results

ATM results

Gibbs Sampling for Collaboration Influence Model

We want , the posterior distribution of topic Z, (author, collaborator) pair X and which favor of collaboration over influence R given the words W in the corpus:

We begin by calculating and :



,

where P is the number of all the different author-collaborator-favor of collaboration combination (a,a',r).

So the Gibbs sampling of  :



Further manipulation can turn the above equation into update equations for the topic and author-collaboration of each corpus token: