Workshop on Objective Bayes Methodology

该奖项支持参加2017年12月10日至13日在德克萨斯州奥斯汀的德克萨斯大学举办的为期四天的“2017年客观贝叶斯研讨会”。研讨会将包括一天的研究生和新研究人员的教程，三天的科学会议和一次海报会议，所有这些都集中在可以自动使用的统计推断方法的主要最新发展，即除了对数据如何产生的程式化的概率描述之外不需要主观输入的方法。客观贝叶斯研讨会是贝叶斯统计学中持续时间最长和卓越的会议之一，讨论范围广泛的主题，包括稳健的、默认的贝叶斯分析、可重复性、变量选择、大数据和非参数贝叶斯方法。目的贝叶斯方法涵盖了从生物统计学到机器学习、渐近性、空间推理和计算等多个领域的研究。讲习班将有助于促进这些不同领域的研究人员之间的互动，目的是提出新的想法和研究方向。有关会议的更多详情，请参阅会议主页https://sites.google.com/site/obayes2017/.

This award supports participation in the four-day "Objective Bayes 2017 Workshop" held at the University of Texas at Austin, TX on December 10-13, 2017. The workshop will consist of one day of tutorials for graduate students and new researchers, three days of scientific sessions, and a poster session, all centering on major recent developments in statistical inference methods that can be used automatically, that is, methodology that does not require subjective input other than a stylized probabilistic description of how the data arises.The Objective Bayes workshop ("O'Bayes") is one of the longest running and preeminent meetings in Bayesian statistics, addressing a wide range of topics including robust, default Bayesian analysis, reproducibility, variable selection, big data, and nonparametric Bayesian methods. Objective Bayes methods encompass research in very diverse areas, from biostatistics, to machine learning, asymptotics, spatial inference, and computation. The workshop will serve to foster interaction of researchers in these diverse areas with an aim of initiating novel ideas and research directions. For more details about the conference please see the conference homepage https://sites.google.com/site/obayes2017/.