Probabilistic matching is a technique used in the context of online advertising to link or match data from different sources, without using a unique identifier or a deterministic method. Probabilistic matching relies on statistical algorithms and machine learning models to infer the likelihood that two data points refer to the same user or entity, based on the presence of shared or overlapping attributes. Probabilistic matching can be an effective way to link or match data from different sources, such as cookies, mobile devices, or offline data, and can provide a more comprehensive and accurate view of a user or audience. Probabilistic matching can also be beneficial for advertisers, as it can help to improve the targeting, measurement, and optimization of an advertising campaign. Probabilistic matching can also be beneficial for publishers and platforms, as it can help to enrich and enhance their audience data and insights, and to provide a better user experience for their audience.