Irving John Good (9 December 1916 – 5 April 2009) [1] [2] was a British mathematician who worked as a cryptologist at Bletchley Park with Alan Turing. After the Second World War, Good continued to work with Turing on the design of computers and Bayesian statistics at …

2009年4月5日 · Irving John Good was an English mathematician who worked at Bletchley Park and at GCHQ and later went on to work with computers and statistics. Irving John Good, whose name was originally Isidore Jacob Gudak, was known to his friends and colleagues as Jack.

欧文·约翰·古德（Irving John Good ）（1916年12月9日至2009年4月5日）是一位英国数学家，他在艾伦·图灵（Alan Turing）曾在布莱奇利公园（Bletchley Park）担任加密医生。第二次世界大战后，良好继续与曼彻...

而统计学家 I.J. Good 于 1965 年提出技术奇点的必要条件──“ 智能爆炸 ” 概念： "让我们将超级智能机器定义为一种能够远远超过任何人的所有智力活动的机器。

Biography of pioneer Irving John Good from J.A.N. Lee's 1995 book Computer Pioneers, updated

J. (“Jack”) Good was a leading Bayesian statistician for more than half a century after World War II, playing an important role in the post-war Bayesian revival. But his graduate training had been in pure mathematics rather than statistics (one of his doctoral advisors at Cambridge had been the famous G. H. Hardy).

Virginia Tech's Irving John (Jack) Good, one of the founders of modern Bayesian inference and a member of the World War II code-breaking team at Bletchley Park, died of natural causes on April 5 in Radford, Va. He was 92. A citizen of the United Kingdom, Good was professor of statistics in the College of Science since 1967.

2020年7月2日 · 1953年古德(I.J.Good)在图灵(Alan Turing)的指导下，提出的在统计中相信可靠的统计数据，而对不可信的统计数据打折扣的一种概率估计方法，同时将折扣出来的那一小部分概率给与未看见的时间(Unseen Events)。

I.J. Good was a life-long chess player, known as one of the top in the world at the time. He became highly interested in the Chinese game of strategy, Go, and helped popularize it in Europe and the US when he wrote an article about it for New Scientist.

Jack Good has made fundamental contributions to mathematics, physics, computer science, philosophy and especially statistics, chiefly famous as a pioneer of Bayesianism, especially hierarchical, and of the Bayes/non-Bayes compromise.