Record matching is a fundamental and ubiquitous part of today?s society. Anything from typing in a password in order to access your email to connecting existing health records in California with new health records in New York requires matching records together. In general, there are two types of record matching algorithms: deterministic, a more rules-based approach, and probabilistic, a model-based approach. Both types have their advantages and disadvantages. If the amount of data is relatively small, deterministic algorithms yield very high success rates. However, the number of common mistakes, and subsequent rules, becomes astronomically large as the sizes of the datasets increase. This leads to a highly labor-intensive process updating and maintaining the matching algorithm. On the other hand, probabilistic record matching implements a mathematical model that can take into account keying mistakes, does not require as much maintenance and over- head, and provides a probability that two particular entities should be linked. At the same time, as a model, assumptions need to be met, fitness has to be assessed, and predictions can be incorrect. Regardless of the type of algorithm, nearly all utilize a 0/1 field-matching structure, including the Fellegi-Sunter algorithm from 1969. That is to say that either the fields match entirely, or they do not match at all. As a result, typographical errors can get lost and false negatives can result. My research has yielded that using Jaro-Winkler string comparator scores as predictors to a Bayesian logistic regression model in lieu of a restrictive binary structure yields marginal improvement over current methodologies.