Thank you for the explanation. I can understand the calculations now,
but I still don't get the meaning. I think I'll try to sleep a night
over it and try again tomorrow.
Best regards,
Thomas
Am 09.05.2011 03:42, schrieb Ted Dunning:
In this notation, k is assumed to be a matrix. k_11 is the element in the
first row and first column.
I used k to sound like count.
The notation that you quote is R syntax. rowSums is a function that
computes the row-wise sums of the argument k. H is a function defined
elsewhere.
On Sun, May 8, 2011 at 6:33 PM, Thomas Söhngen<[email protected]> wrote:
Thank you for the blog post and showing me the G-test formula.
After going through your blog post, I still have some open questions: You
introduce k_11 to k_22, but I don't understand what "k" itself actually
stands for in your formular and how the sums are defined: LLR = 2 sum(k)
(H(k) - H(rowSums(k)) - H(colSums(k)))
Am 09.05.2011 02:46, schrieb Ted Dunning:
My guess is that the OP was asking about the generalized log-likelihood
ratio test used in the Mahout recommendation framework.
That is a bit different from what you describe in that it is the log of
the
ratio of two maximum likelihoods.
See http://en.wikipedia.org/wiki/G-test for a definition of the test used
in
Mahout.
On Sun, May 8, 2011 at 5:43 PM, Jeremy Lewi<[email protected]> wrote:
Thomas,
Are you asking a general question about log-likelihood or a specific
implementation usage in Mahout?
In general the likelihood is just a number, between 0 and 1 which
measures the probability of observing some data under some distribution.
