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 <ted.dunning@gmail.com> 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/surpriseandcoincidence.html for
> a
> detailed description.
>
>
>
>
> On Wed, Apr 30, 2014 at 11:31 PM, Mario Levitin <mariolevitin@gmail.com
> >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 <ted.dunning@gmail.com>
> > wrote:
> >
> > > Excellent. Look forward to hearing your reactions.
> > >
> > > On Mon, Apr 28, 2014 at 1:14 AM, Mario Levitin <mariolevitin@gmail.com
> > > >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.
> > > >
> > >
> >
>
