AI Article Synopsis
- The paper introduces a framework designed to integrate diverse data sets using a probabilistic approach, highlighting the need for such modeling beyond just compiling data from databases.
- The framework accommodates various types of experimental data, which often cannot be simply summarized or categorized, and acknowledges that doing so may not be suitable.
- It emphasizes the importance of incorporating prior knowledge into the modeling process and allows for flexible extensions to support more common data-driven analysis techniques.
Article Abstract
In this paper we present a framework for integrating diverse data sets under a coherent probabilistic setup. The necessity of a probabilistic modeling arises from the fact that data integration does not restrict to compiling information from data bases with data that are typically thought to be non-random. Currently wide range of experimental data is also available however rarely these data sets can be summarized in simple output data, e.g. in categorical form. Moreover it may not even be appropriate to do so. The proposed setup allows modeling not only the observed data and parameters of interest but most importantly to incorporate prior knowledge. Additionally the setup easily extends to facilitate more popular data-driven analysis.
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