PROBABILISTIC MODELING FOR ANONYMIZED DATA INTEGRATION AND BAYESIAN SURVEY MEASUREMENT OF SPARSE AND WEAKLY-LABELED DATASETS

    公开(公告)号:US20210019603A1

    公开(公告)日:2021-01-21

    申请号:US16886379

    申请日:2020-05-28

    摘要: Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to perform probabilistic modeling for anonymized data integration and measurement of sparse and weakly-labeled datasets are disclosed. An apparatus includes a training controller to train a neural network to produce a trained neural network to output model parameters of a probability model, a model evaluator to execute the trained neural network on input data specifying a time of day, a media source, and at least one feature different from the time of day and the media source to determine one or more first model parameters of the probability model, and a ratings metric generator to evaluate the probability model based on input census data to determine a ratings metric corresponding to the time of day, the media source, and the at least one feature, the probability model configured with the one or more first model parameters.