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第2行： − Glymour is the founder of the Philosophy Department at Carnegie Mellon University, a [[Guggenheim Fellowship|Guggenheim Fellow]], a Fellow of the Center for Advanced Study in Behavioral Sciences,<ref>{{cite web|url=https://casbs.stanford.edu/news/awards-and-elections-fall-2019|title=Awards and Elections, Fall 2019|publisher=Center for Advanced Study in Behavioral Sciences|accessdate=December 16, 2019}}</ref> a [[Phi Beta Kappa Society|Phi Beta Kappa]] lecturer,<ref>{{cite web|url=https://www.pbk.org/Awards/Romanell/PastWinners|title=Romanell-Phi Beta Kappa Professorship Past Winners|publisher=Phi Beta Kappa|accessdate=December 16, 2019}}</ref> and is a Fellow of the statistics section of the AAAS.<ref>{{cite web|url=https://www.amacad.org/person/clark-glymour|title=Clark Glymour|publisher=American Academy of Arts and Sciences|accessdate=December 16, 2019}}</ref> Glymour and his collaborators created the causal interpretation of Bayes nets.<ref>P. Spirtes, C. Glymour, R. Scheines, Causation, Prediction and Search, Springer Lecture Notes in Statistics, 1993.</ref> His areas of interest include [[epistemology]]<ref>Epistemology: 5 Questions Edited by Vincent F. Hendricks and Duncan Pritchard, September 2008, {{ISBN|87-92130-07-0}}.</ref> (particularly [[Android epistemology]]), [[machine learning]], [[automated reasoning]], [[psychology]] of judgment, and [[mathematical psychology]].<ref>{{cite web|url=https://www.ihmc.us/groups/clark-glymour/|title=Clark Glymour|accessdate=December 16, 2019}}</ref> One of Glymour's main contributions to the philosophy of science is in the area of [[Bayesian probability]], particularly in his analysis of the Bayesian "problem of old evidence".<ref>{{cite web|url=http://plato.stanford.edu/entries/epistemology-bayesian/|title=Bayesian Epistemology|date=July 12, 2001}}</ref><ref>Glymour, C.; Theory and evidence (1981), pp. 63-93.</ref> Glymour, in collaboration with Peter Spirtes and Richard Scheines, also developed an automated causal inference algorithm implemented as software named [[TETRAD]].<ref>[http://www.phil.cmu.edu/projects/tetrad/publications.html Publications] TETRAD. Retrieved December 16, 2019.</ref> Using multivariate statistical data as input, TETRAD rapidly searches from among all possible causal relationship models and returns the most plausible causal models based on conditional dependence relationships between those variables. The algorithm is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence.<ref>Glymour, Clark; Scheines, Richard; Spirtes, Peter; Kelly, Kevin. "TETRAD: Discovering Causal Structure" Multivariate Behavioral Research 23.2 (1988). 10 July 2010. {{DOI|10.1207/s15327906mbr2302_13}}. {{PMID|26764954}}.</ref> − Glymour is the founder of the Philosophy Department at Carnegie Mellon University, a Guggenheim Fellow, a Fellow of the Center for Advanced Study in Behavioral Sciences, a Phi Beta Kappa lecturer, and is a Fellow of the statistics section of the AAAS. Glymour and his collaborators created the causal interpretation of Bayes nets.P. Spirtes, C. Glymour, R. Scheines, Causation, Prediction and Search, Springer Lecture Notes in Statistics, 1993. His areas of interest include epistemologyEpistemology: 5 Questions Edited by Vincent F. Hendricks and Duncan Pritchard, September 2008, . (particularly Android epistemology), machine learning, automated reasoning, psychology of judgment, and mathematical psychology. One of Glymour's main contributions to the philosophy of science is in the area of Bayesian probability, particularly in his analysis of the Bayesian "problem of old evidence".Glymour, C.; Theory and evidence (1981), pp. 63-93. Glymour, in collaboration with Peter Spirtes and Richard Scheines, also developed an automated causal inference algorithm implemented as software named TETRAD.Publications TETRAD. Retrieved December 16, 2019. Using multivariate statistical data as input, TETRAD rapidly searches from among all possible causal relationship models and returns the most plausible causal models based on conditional dependence relationships between those variables. The algorithm is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence.Glymour, Clark; Scheines, Richard; Spirtes, Peter; Kelly, Kevin. "TETRAD: Discovering Causal Structure" Multivariate Behavioral Research 23.2 (1988). 10 July 2010. . . − 格莱默是卡内基梅隆大学哲学系的创始人，古根海姆研究员，行为科学高级研究中心研究员，斐陶斐学会讲师，美国科学促进会统计部门研究员。和他的合作者创造了贝叶斯网络的因果解释。斯皮尔特斯，c. 格莱莫尔，r. 舍因斯，因果关系，预测和搜索，斯普林格统计讲义，1993。他的兴趣领域包括: 由 Vincent f. Hendricks 和 Duncan Pritchard 编辑的《认识论/认识论: 5个问题》 ，2008年9月，。(尤其是 Android 认识论)、机器学习、自动推理、判断心理学和数学心理学。格莱莫尔对科学哲学的主要贡献之一是在贝叶斯概率领域，特别是在他对贝叶斯“旧证据问题”的分析中。理论与证据(1981) ，页。63-93.格莱莫尔与彼得 · 斯皮尔茨和理查德 · 谢恩斯合作，还开发了一种自动因果推理算法，称为 TETRAD.Publications TETRAD。16,2019.利用多元统计数据作为输入，TETRAD 从所有可能的因果关系模型中快速搜索，并根据这些变量之间的条件依赖关系返回最合理的因果模型。该算法基于统计学、图论、科学哲学和人工智能的原理。格莱莫尔，克拉克; 舍因斯，理查德; Spirtes，彼得; 凯利，凯文。“四合一: 发现因果结构”多元行为研究23.2(1988)。二零一零年七月十日。.+ − Glymour earned undergraduate degrees in [[chemistry]] and [[philosophy]]. He did graduate work in [[chemical physics]] and obtained a Ph.D in History and Philosophy of Science from [[Indiana University (Bloomington)|Indiana University]] in 1969. − Glymour earned undergraduate degrees in chemistry and philosophy. He did graduate work in chemical physics and obtained a Ph.D in History and Philosophy of Science from Indiana University in 1969.+ − − 获得了化学和哲学的本科学位。他毕业于化学物理学，并于1969年获得印第安纳大学历史与科学哲学博士学位。
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==工作经历==
==工作经历==
格莱默是卡内基梅隆大学哲学系的创始人，古根海姆研究员（[[Guggenheim Fellowship|Guggenheim Fellow]]），行为科学高级研究中心研究员<ref>{{cite web|url=https://casbs.stanford.edu/news/awards-and-elections-fall-2019|title=Awards and Elections, Fall 2019|publisher=Center for Advanced Study in Behavioral Sciences|accessdate=December 16, 2019}}</ref>，[[Phi Beta Kappa Society|Phi Beta Kappa]]联谊会讲师<ref>{{cite web|url=https://www.pbk.org/Awards/Romanell/PastWinners|title=Romanell-Phi Beta Kappa Professorship Past Winners|publisher=Phi Beta Kappa|accessdate=December 16, 2019}}</ref>，美国科学促进会（AAAS）统计部门研究员<ref>{{cite web|url=https://www.amacad.org/person/clark-glymour|title=Clark Glymour|publisher=American Academy of Arts and Sciences|accessdate=December 16, 2019}}</ref>。格莱默和他的合作者创造了贝叶斯网络的因果解释<ref>P. Spirtes, C. Glymour, R. Scheines, Causation, Prediction and Search, Springer Lecture Notes in Statistics, 1993.</ref>。他的研究兴趣领域包括: 认识论（[[epistemology]]）<ref>Epistemology: 5 Questions Edited by Vincent F. Hendricks and Duncan Pritchard, September 2008, {{ISBN|87-92130-07-0}}.</ref>(尤其是 Android 认识论（[[Android epistemology]]）)、机器学习（[[machine learning]],）、自动推理（[[automated reasoning]]）、判断心理学（[[psychology]] of judgment）和数学心理学（[[mathematical psychology]]）。<ref>{{cite web|url=https://www.ihmc.us/groups/clark-glymour/|title=Clark Glymour|accessdate=December 16, 2019}}</ref>格莱莫尔对科学哲学的主要贡献之一是在贝叶斯概率（[[Bayesian probability]]）领域，特别是在他对贝叶斯“旧证据问题”的分析中<ref>{{cite web|url=http://plato.stanford.edu/entries/epistemology-bayesian/|title=Bayesian Epistemology|date=July 12, 2001}}</ref><ref>Glymour, C.; Theory and evidence (1981), pp. 63-93.</ref>。格莱默与彼得 · 斯皮尔茨（Peter Spirtes）和理查德 · 谢恩斯（Richard Scheines）合作，还开发了一种自动因果推理算法，以软件形式实现，命名为[[TETRAD]]<ref>[http://www.phil.cmu.edu/projects/tetrad/publications.html Publications] TETRAD. Retrieved December 16, 2019.</ref>。采用多元统计数据作为输入，TETRAD 从所有可能的因果关系模型中快速搜索，并根据这些变量之间的条件依赖关系输出最合理的因果模型。该算法基于统计学、图论、科学哲学和人工智能的原理<ref>Glymour, Clark; Scheines, Richard; Spirtes, Peter; Kelly, Kevin. "TETRAD: Discovering Causal Structure" Multivariate Behavioral Research 23.2 (1988). 10 July 2010. doi:[https://doi.org/10.1207%2Fs15327906mbr2302_13 10.1207/s15327906mbr2302_13]. [[wikipedia:PMID_(identifier)|PMID]] [https://pubmed.ncbi.nlm.nih.gov/26764954 26764954].</ref>。
格莱默获得了化学（[[chemistry]]）和哲学（[[philosophy]]）的本科学位。他研究生工作专注于化学物理学（[[chemical physics]]），并于1969年获得印第安纳大学（ [[Indiana University (Bloomington)|Indiana University]]）历史与科学哲学博士学位。
==研究成果(Publications)==
==研究成果(Publications)==