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<p>This paper demonstrates that in an important class of data-integration problems even Big Data cannot speak for itself: marginal probabilities provided by large and unbiased datasets do not determine a unique joint probability distribution. Big Data must instead be accompanied by further reasoning methodologies for handling unavoidable uncertainties. I assess how Bayesian approaches handle this underdetermination and argue that they face practical challenges when dealing with Big Data that does not speak for itself.</p>

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Even big data does not speak for itself

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Jürgen Landes

摘要

This paper demonstrates that in an important class of data-integration problems even Big Data cannot speak for itself: marginal probabilities provided by large and unbiased datasets do not determine a unique joint probability distribution. Big Data must instead be accompanied by further reasoning methodologies for handling unavoidable uncertainties. I assess how Bayesian approaches handle this underdetermination and argue that they face practical challenges when dealing with Big Data that does not speak for itself.