Workshop on Objective Bayes Methodology

该奖项支持参加2017年12月10日至13日在德克萨斯大学奥斯汀分校举行的为期四天的“客观贝叶斯2017研讨会”。研讨会将包括一天的研究生和新研究人员教程，三天的科学会议和海报会议，所有这些都集中在统计推断方法的主要最新发展，可以自动使用，即，一种不需要主观输入的方法，只需要对数据如何产生进行程式化的概率描述。（“O'Bayes”）是贝叶斯统计学中运行时间最长和最杰出的会议之一，涉及广泛的主题，包括鲁棒性，默认贝叶斯分析，再现性，变量选择，大数据和非参数贝叶斯方法。 贝叶斯方法涵盖了从生物统计到机器学习、渐近、空间推理和计算等非常不同领域的研究。讲习班将促进这些不同领域的研究人员之间的互动，以提出新的想法和研究方向。有关会议的更多详细信息，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/.