sorry, clicked on wrong thread. please disregard.
On Wed, Apr 30, 2014 at 4:24 PM, Dmitriy Lyubimov <[email protected]> wrote: > sure. I assume this should include statements that something crushes > something without providing a link to a published analysis of what it is > something that crushes something another and due to what something. > > > On Wed, Apr 30, 2014 at 4:21 PM, Ted Dunning <[email protected]>wrote: > >> OK. >> >> Whether a user has interacted with A is a sample from a binomial >> distribution with an unknown parameter p_A. Likewise with B and p_B. The >> two binomial distributions may or may not be independent. >> >> The LLR is measuring the degree evidence against independence. >> >> >> >> >> On Thu, May 1, 2014 at 12:50 AM, Mario Levitin <[email protected] >> >wrote: >> >> > 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 <[email protected]> >> 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.htmlfor >> > > a >> > > detailed description. >> > > >> > > >> > > >> > > >> > > On Wed, Apr 30, 2014 at 11:31 PM, Mario Levitin < >> [email protected] >> > > >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 <[email protected] >> > >> > > > wrote: >> > > > >> > > > > Excellent. Look forward to hearing your reactions. >> > > > > >> > > > > On Mon, Apr 28, 2014 at 1:14 AM, Mario Levitin < >> > [email protected] >> > > > > >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. >> > > > > > >> > > > > >> > > > >> > > >> > >> > >
