From: abhisheksgumadi@gmail.com   
      
   On Dec 10, 7:51 am, Rob  wrote:   
   > I just started to read this paper and got confused by the generative   
   > process of LDA.   
   >   
   > 1. Choose \theta = Dir(\alpha)   
   >  This might be the only part I understand. \theta is a length-K   
   > vector(distribution)   
   > if there are K predefined topics.   
   >   
   > 2. for each of the N words w_n:   
   > (a)choose a topic z_n = mult(\theta)   
   > What does this mean? The probability of word w_n to   
   > topic z_n? If it is a multinomial distribution, there should   
   > be a count x_k besides P(x_k) = \theta_k. Otherwise,   
   > how can I calculate this multinomial probability?   
   >   
   > (b) choose a word w_n from p(w_n|z_n,\beta)   
   >    This is the most confusing part. Since the Step 2 is "for each word   
   > w_n",   
   >  why are we "choose a word w_n" here again? I cannot understand this.   
   > Or is this the conditional probability of w_n given z_n and   
   > parameters?   
   >   
   > I'm confused by the generative process, how do you actually "generate"   
   > words   
   > in real application, aren't they contained in the document? For   
   > example, if given   
   > the following training set   
   >   
   >             w_1  w_2   w_3   w_4   
   >     d1      1     0        3       5   
   >     d2      0     3        1       2   
   > .............................................   
   >   
   > what does the generative process look like? Anyone can help give a   
   > walkthrough example?   
   > Great thanks.   
   >   
      
   Are you looking at Vidit Jain's paper by chance?   
      
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