Probabilistic matching
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 probabilistic matching means
Probabilistic matching accumulates evidence. Each comparison — names agree exactly, postcodes agree, countries disagree, incorporation years differ by one — contributes a weight, positive or negative, and the weights sum to a score which is mapped to a probability. Two records may match on a weak field and still fail; they may disagree on a weak field and still pass, if the strong fields carry them.
The classical form is Fellegi–Sunter, whose weights are derived from two per-field probabilities: how often the field agrees among true matches, and how often it agrees by chance among non-matches. Modern implementations often fit the same weights by regression against labelled pairs instead, which trades the elegance of the derivation for the ability to model interactions and to fit on the data actually being linked.
Either way the output is a number, and a number alone is not a decision. Thresholds turn it into one, and they are chosen against a labelled set, with the error rate at each candidate threshold measured before one is adopted.
Entity Bind fits its weights by L2-regularised logistic regression against a labelled gold set, publishes the out-of-fold analysis of the fit rather than assuming it generalised, and applies a three-region policy. The intercept is not treated as universal: it is the log-odds of a match before any feature fires on that gold set's own base rate, and a deployment whose candidate generator produces a different match rate needs a prior shift rather than a copied constant.
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
- How it worksThe seven-stage cascade in order, what each stage can settle on its own, and what it hands to the next.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
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.
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.
m-probability
The m-probability of a comparison field is the probability that the field agrees given that the pair really is a match — in effect, one minus the rate at which that field is wrong, missing or mistyped in true matches.
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.
Deterministic matching
Deterministic matching decides a pair by an explicit rule — usually agreement on a strong identifier — and produces the same verdict every time with no score in between.
Calibration
A confidence score is calibrated when it matches observed reality — of the decisions returned at 0.95, about 95% should be correct — which is a different property from being accurate, and a much rarer one.
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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