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21 世纪的贝叶斯主义者：现代应用贝叶斯统计学实践中的机遇与挑战。

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.

机构信息

Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.

School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.

出版信息

Philos Trans A Math Phys Eng Sci. 2023 May 15;381(2247):20220156. doi: 10.1098/rsta.2022.0156. Epub 2023 Mar 27.

DOI:10.1098/rsta.2022.0156
PMID:36970822
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10041356/
Abstract

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

摘要

在过去三十年中，基于哲学、理论、方法和计算的坚实基础，贝叶斯方法现在已经成为大多数统计学家和数据科学家工具包的一个组成部分。无论他们是专注的贝叶斯人还是机会主义的使用者，应用专业人员现在都可以从贝叶斯范式提供的许多好处中受益。在本文中，我们探讨了应用贝叶斯统计学中的六个现代机会和挑战：智能数据收集、新数据源、联合分析、隐式模型推断、模型转移和有目的的软件产品。本文是主题为“贝叶斯推断：挑战、观点和前景”的一部分。

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