The u probabilities are relatively easy to estimate if we can assume that the overwhelming majority of comparisons are non-matches in the set of all comparisons. This assumption typically holds.

Where this is the case we can simply take a random sample of input rows and compute the cartesian product to generate all possible comparisons.

We then compute the U values by assuming that all these comparisons are non-matches.

We have an implementation of this approach in Splink, a piece of software which estimates the Fellegi Sunter model

The u probabilities are relatively easy to estimate if we can assume that the overwhelming majority of comparisons are non-matches in the set of all comparisons. This assumption typically holds.

Where this is the case we can simply take a random sample of input rows and compute the cartesian product to generate all possible comparisons.

We then compute the U values by assuming that all these comparisons are non-matches.

We have an implementation of this approach in Splink, a piece of software which estimates the Fellegi Sunter model. The relevant code is here.