m-probability
The definition as the field writes it comes first, weaknesses and all. What Entity Bind does about it comes last, in its own paragraph, so you can take one without the other. Filed under matching methods.
How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance.
every figure, with its population →What m-probability means
In the Fellegi–Sunter model, m is estimated per comparison field and measures data quality rather than discriminating power. If names agree in 92% of true matches, the name field's m-probability is 0.92, and the remaining 8% is the rate of typos, trading names, rebrandings and abbreviations in the population being linked.
A high m means a field is reliable when the pair is genuinely the same entity. A low m means agreement is often absent even for true matches, which makes disagreement weak evidence of anything — this is exactly why a disagreeing telephone number should not sink a pair while a disagreeing registration number should.
m is customarily estimated by expectation-maximisation over unlabelled candidate pairs, by hand from domain knowledge, or directly from labelled data where labels exist. All three are estimates of the population being linked, not universal constants: the m-probability of 'address agrees' is much lower in a file of supplier records collected over ten years than in two register extracts read the same week.
Entity Bind does not estimate m and u separately. Its weights come from a regularised logistic fit against a labelled gold set, so each feature carries a single fitted log-odds coefficient that plays the role the m/u ratio plays in the classical model. The trade is deliberate: the fitted form models interactions the independence assumption cannot, and it costs the direct interpretability of m as a data-quality statistic.
Source
- Fellegi and Sunter (1969), “A Theory for Record Linkage”, Journal of the American Statistical Association 64(328), 1183–1210
The paper behind the three-region rule and the m and u probabilities. Its optimality result holds under the model's assumptions, conditional independence between fields among them, which real company data usually breaks.
Where this shows up in Entity Bind
Three pages where this term stops being vocabulary and starts changing an answer, a payload or a price.
- Fellegi-Sunter explained, with worked arithmeticThe Fellegi-Sunter model, published in 1969, is the framework almost every probabilistic matcher is a variation on. Its idea is simple and still correct: for each field…Open
- The accuracy methodEvery published rate with the population it was measured over, its interval and its fixture — including the input shapes that fail.Open
- Compared with SplinkThe open-source library most teams reach for first, which learns how much each agreeing field is worth — the Fellegi–Sunter model — and what it does better than we do.Open
The fastest way past a definition is a file: which of your rows are the same company, which of your existing matches we would not have made, and what we could not decide.
Start freeThe terms next to this one
Each with its own sentence, because the next question is usually about the word beside this one.
u-probability
The u-probability of a comparison field is the probability that the field agrees by chance given that the pair is not a match — a measure of how common a value is, and therefore of how little an agreement proves.
Fellegi–Sunter
Fellegi–Sunter is the 1969 statistical framework for record linkage that scores a candidate pair as a log-likelihood ratio of field agreements and splits the result into three regions: accept, reject, and refer for review.
Match weight
A match weight is the log-scaled contribution one comparison makes to a pair's score, positive when agreement is evidence of a match and negative when disagreement is evidence against.
Probabilistic matching
Probabilistic matching scores a candidate pair by weighing the agreement and disagreement of several fields into a single number, then applies thresholds to that number rather than to any one field.
The other 12 terms on this shelf
How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance.
Internal fixtures only. The historical 2026-08-29 per-tier recording covers 317 GB companies with identifier-rich arrivals seeding the catalogue first; the context tier includes country and registered-town prose. Its legacy replay has no full-prompt-verified calls and cannot certify current calibration. The September 5 CI reports cover 1,176 labelled pairs and 705 records over 250 companies using offline stubs. These are not independent customer holdouts or live production validation; the 99% minimum-input product target remains unmet. Scope is company and organisation identity only — not people, vessels, bank-account ownership, sanctions or emissions. Results may not be used for eligibility decisions about individuals, including employment, credit and tenancy.
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