Ted, I understand how the contingency table is constructed, and how to compute the LLR value. What I cannot understand is how to link this with binomial distributions. On Thu, May 1, 2014 at 1:02 AM, Ted Dunning wrote: > The contingency table is constructed by looking at how many users have > expressed preference or interest in two items. If the items are A and B, > the pertinent counts are > > k11 - the number of users who interacted with both A and B > k12 - the number of users who interacted with A but not B > k21 - the number of users who interacted with B but not A > k22 - the number of users who interacted with neither A nor B. > > These values are values that go into the contingency table and are all that > is needed to compute the LLR value. > > See http://tdunning.blogspot.de/2008/03/surprise-and-coincidence.html for > a > detailed description. > > > > > On Wed, Apr 30, 2014 at 11:31 PM, Mario Levitin >wrote: > > > Hi Ted, > > I have read the paper. I understand the "Likelihood Ratio for Binomial > > Distributions" part. > > However, I cannot make a connection with this part and the contingency > > table. > > > > In order to calculate Likelihood Ratio for two Binomial Distributions you > > need the values: p, p1, p2, k1, k2, n1, n2. > > But the information contained in the contingency table are different from > > these values. So, again, I do not understand how the information > contained > > in the contingency table is linked with Likelihood Ratio for Binomial > > Distributions. > > > > In order to find the similarity between two users I tend to think of the > > boolean preferences of user1 as a sample from a binomial distribution and > > the boolean preferences of user2 as another sample from a binomial > > distribution. Then use the LLR to assess how likely these distributions > are > > the same. But I don't think this is correct since this calculation does > not > > use the contingency table. > > > > I hope my question is clear. > > Thanks. > > > > > > > > On Mon, Apr 28, 2014 at 2:41 AM, Ted Dunning > > wrote: > > > > > Excellent. Look forward to hearing your reactions. > > > > > > On Mon, Apr 28, 2014 at 1:14 AM, Mario Levitin > > >wrote: > > > > > > > Not yet, but I will. > > > > > > > > > > > > > > Have you read my original paper on the topic of LLR? It explains > the > > > > > connection with chi^2 measures of association. > > > > > > > > > >