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| 136 |
probability functions we can write the information radius as follows:The interesting interpretation of the information radius that I referred to above is illustrated most easily in terms of continuous probability functions
...R u 1,u 2 v uI u 1 v vI u 2 v where I u i v measures the expectation on u i of the information in favour of rejecting v for u i given by making an observation;it may be regarded as the information gained from being told to reject v in favour of u i
...thereby removing the arbitrary v
...v u u 1 v u 2 that is,an average of the two distributions to be discriminated
...p x p x w 1 P w 1 p x w 2 P w 2 defined over the entire collection without regard to relevance
...There is one technical problem associated with the use of the information radius,or any other discrimination measure based on all four cells of the contingency table,which is rather difficult to resolve
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| 127 |
function
...One important reason for having estimation rules different from the simple x n,is that this is rather unrealistic for small samples
...0 <p <1
...This is really as much as I wish to say about estimation rules,and therefore I shall not push the technical discussion on this points any further;the interested reader should consult the readily accessible statistical literature
...Recapitulation At this point I should like to summarise the formal argument thus far so that we may reduce it to simple English
...The first point to make then,is that,we have been trying to estimate P relevance document,that is,the probability of relevance for a given document
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