Probability Theory from a Programming Perspective (Invited Paper)

Sam Staton

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Probability Theory from a Programming Perspective (Invited Paper)

机译：从编程角度看概率论（特邀论文）

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A leading idea is to apply techniques from verification and programming theory to machine learning and statistics, to deal with things like compositionality and various notions of correctness and complexity. Probabilistic programming is an example of this. Moreover, this approach leads to new foundational methods in probability theory. This is particularly true in the "non-parametric" aspects, for example in higher-order functions and infinite random graph models.

机译：一个领先的想法是将验证和编程理论中的技术应用于机器学习和统计，以处理诸如组成性以及各种正确性和复杂性概念。概率编程就是一个例子。而且，这种方法导致了概率论中新的基础方法。这在“非参数”方面尤其如此，例如在高阶函数和无限随机图模型中。

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《LIPIcs : Leibniz International Proceedings in Informatics》 |2018年第23期|共1页
作者
Sam Staton;
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中图分类计算技术、计算机技术;
关键词
correctnesscomplexitystatistics;

机译：正确性;复杂度统计;

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Probability Theory from a Programming Perspective (Invited Paper)

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