To analyze data in applications, it is necessary to form statistics that allow us to draw conclusions about what the data represents. Machine/deep learning approaches also form statistics within their black boxes and in order to understand these methods, we need to consider what statistics those learning algorithms might be using. In both cases, we need to determine whether the statistic being used is sufficient to draw conclusions.

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Hypothesis Testing, Estimation, Information, and Sufficient Statistics

  • Jerry D. Gibson

摘要

To analyze data in applications, it is necessary to form statistics that allow us to draw conclusions about what the data represents. Machine/deep learning approaches also form statistics within their black boxes and in order to understand these methods, we need to consider what statistics those learning algorithms might be using. In both cases, we need to determine whether the statistic being used is sufficient to draw conclusions.