Pointwise mutual information
||This article provides insufficient context for those unfamiliar with the subject. (February 2012)|
The PMI of a pair of outcomes x and y belonging to discrete random variables X and Y quantifies the discrepancy between the probability of their coincidence given their joint distribution and their individual distributions, assuming independence. Mathematically:
The mutual information (MI) of the random variables X and Y is the expected value of the PMI over all possible outcomes (with respect to the joint distribution ).
Finally, will increase if is fixed but decreases.
Here is an example to illustrate:
Using this table we can marginalize to get the following additional table for the individual distributions:
With this example, we can compute four values for . Using base-2 logarithms:
(For reference, the mutual information would then be 0.214170945)
Similarities to mutual information
Pointwise Mutual Information has many of the same relationships as the mutual information. In particular,
Where is the self-information, or .
Normalized pointwise mutual information (npmi)
Pointwise mutual information can be normalized between [-1,+1] resulting in -1 (in the limit) for never occurring together, 0 for independence, and +1 for complete co-occurrence.
Chain-rule for pmi
Pointwise mutual information follows the chain rule, that is,
This is easily proven by:
||This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. (February 2012)|
- Bouma, Gerloff (2009). "Normalized (Pointwise) Mutual Information in Collocation Extraction". Proceedings of the Biennial GSCL Conference.
- Fano, R M (1961). "chapter 2". Transmission of Information: A Statistical Theory of Communications. MIT Press, Cambridge, MA.
- Demo at Rensselaer MSR Server (PMI values normalized to be between 0 and 1)