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- W152330003 abstract "The general problem of data integration is expressed into that of combining individual probabilistic information into a joint posterior probability. Any such combination of information necessarily requires taking into account redundancy within the information utilized. It is shown that the tau model (Journel 2002) can provide an exact analytical representation of such combination. The tau weights express data redundancy for each specific sequence of data conditioning. Instead of using this exact definition of the tau weights, a more practical calibration-based method is proposed. The method requires a prior ranking of the data based on their information content, then the tau weights are approximated by a function of the correlation of each datum with the single most informative one. Such calibration would require training information in the form of joint vectorial data. The tau model can also be expressed as a log-linear estimator of the distance to the unknown event. This definition requires considering the distances (or equivalently the odds ratios) as random variables themselves. An application to binary data is presented." @default.
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- W152330003 date "2005-01-01" @default.
- W152330003 modified "2023-10-13" @default.
- W152330003 title "Evaluating Information Redundancy Through the Tau Model" @default.
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- W152330003 doi "https://doi.org/10.1007/978-1-4020-3610-1_108" @default.
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