--- title: "Probabilistic matching" summary: "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." canonical_url: https://entitybind.com/glossary/probabilistic-matching last_updated: 2026-10-15 copy: full --- # 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. Canonical page: https://entitybind.com/glossary/probabilistic-matching Last updated: 2026-10-15 Glossary · Matching methods ## How to read this 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](https://entitybind.com/glossary#matching). By [James Rooney](https://entitybind.com/about), founder of Entity Bind · Last revised 15 October 2026 ## 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 arithmetic](https://entitybind.com/guides/fellegi-sunter-explained): The 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… - [How it works](https://entitybind.com/how-it-works): The seven-stage cascade in order, what each stage can settle on its own, and what it hands to the next. - [The accuracy method](https://entitybind.com/accuracy): Every published rate with the population it was measured over, its interval and its fixture — including the input shapes that fail. 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 free →](https://app.entitybind.com/app?signup=1) ## The terms next to this one Each with its own sentence, because the next question is usually about the word beside this one. - [Fellegi–Sunter](https://entitybind.com/glossary/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](https://entitybind.com/glossary/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](https://entitybind.com/glossary/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](https://entitybind.com/glossary/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](https://entitybind.com/glossary/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](https://entitybind.com/glossary/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. ### Answered at length elsewhere - [Splink](https://entitybind.com/compare/splink) (Comparison): Free, open source, and it weighs the evidence properly: Fellegi–Sunter, done right. Our scoring stage is the same family. ## 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. - [Normalisation](https://entitybind.com/glossary/normalisation) - [Transliteration](https://entitybind.com/glossary/transliteration) - [Blocking](https://entitybind.com/glossary/blocking) - [Candidate generation](https://entitybind.com/glossary/candidate-generation) - [Deterministic matching](https://entitybind.com/glossary/deterministic-matching) - [Fellegi–Sunter](https://entitybind.com/glossary/fellegi-sunter) - [m-probability](https://entitybind.com/glossary/m-probability) - [u-probability](https://entitybind.com/glossary/u-probability) - [Match weight](https://entitybind.com/glossary/match-weight) - [Fuzzy matching](https://entitybind.com/glossary/fuzzy-matching) - [Edit distance](https://entitybind.com/glossary/edit-distance) - [Jaro–Winkler](https://entitybind.com/glossary/jaro-winkler) Previous, alphabetically: [Precision](https://entitybind.com/glossary/precision) · Next, alphabetically: [Provenance](https://entitybind.com/glossary/provenance) ## Evidence basis 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. ## Start with your own company records. Create your workspace, verify your email and run your first company file. The Free plan needs no card. Create API keys in your workspace when you need an integration. [Start free](https://app.entitybind.com/app?signup=1) · [All 64 terms](https://entitybind.com/glossary) 300 credits. No card. For a guided review of your file, explore [the Merge Safety Sprint](https://entitybind.com/audit-pack). --- Entity Bind resolves companies and organisations only. It does not resolve people, vessels, bank-account ownership, sanctions exposure, emissions or geolocation, and its results may not be used for eligibility decisions about individuals, including employment, credit and tenancy. This is the Markdown copy of https://entitybind.com/glossary/probabilistic-matching, assembled from the same data the page renders and carrying every sentence, table row, figure and question the page's main content carries. The HTML page is canonical; it adds the layout, the navigation and the interactive parts (the ask panel, the forms), which a copy can name but not run. Every page on the site, with a line on each: https://entitybind.com/llms.txt.