BayesFellegiSunterModellen

BayesFellegiSu is a hypothetical statistical framework that combines Bayesian inference with principles from the Fellegi-Sunter record linkage model. The Fellegi-Sunter model is a probabilistic approach used to identify and link records that refer to the same entity across different data sources, even when records are not perfectly matched. It assigns weights to agreements and disagreements on various fields and uses these weights to determine the probability that a pair of records are a match.

Bayesian inference, on the other hand, provides a framework for updating beliefs in light of new evidence. It uses probability distributions to represent uncertainty about parameters and outcomes. BayesFellegiSu would likely aim to integrate Bayesian methods into the Fellegi-Sunter framework to improve the estimation of matching probabilities and the handling of uncertainty. This could involve using prior distributions for the Fellegi-Sunter parameters, such as the probability of agreement for matching and non-matching fields, and then updating these priors with observed data.

Such a framework might offer advantages in situations with complex data structures, incomplete information, or when incorporating prior knowledge is important. The Bayesian approach could provide more robust estimates of match/non-match probabilities and allow for a more nuanced quantification of uncertainty associated with the linkage decisions. The development of BayesFellegiSu could lead to more sophisticated and flexible record linkage solutions for various applications, including census data, administrative records, and commercial databases.