评论:简要介绍大数据规模下贝叶斯计算在数据科学中的现状

D. Draper, Alexander Terenin
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引用次数: 1

摘要

我们希望对“比较分布式贝叶斯计算的共识蒙特卡罗策略”的讨论做出贡献,通过提供我们对当前贝叶斯计算的最佳方法的看法,无论是在大数据规模还是在较小的数据集上,如表1所示。这个表格当然是对一个高度复杂的研究领域的过度简化,这个领域在不断变化(现在和可能的未来),但我们认为,尽管有缺点,但构建这种类型的摘要是值得的,如果只是为了促进进一步的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comment: A brief survey of the current state of play for Bayesian computation in data science at Big-Data scale
We wish to contribute to the discussion of "Comparing Consensus Monte Carlo Strategies for Distributed Bayesian Computation" by offering our views on the current best methods for Bayesian computation, both at big-data scale and with smaller data sets, as summarized in Table 1. This table is certainly an over-simplification of a highly complicated area of research in constant (present and likely future) flux, but we believe that constructing summaries of this type is worthwhile despite their drawbacks, if only to facilitate further discussion.
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