{"id":312,"date":"2025-02-20T11:48:31","date_gmt":"2025-02-20T15:48:31","guid":{"rendered":"https:\/\/sites.nd.edu\/bei-hu\/?page_id=312"},"modified":"2026-08-25T12:14:33","modified_gmt":"2026-08-25T16:14:33","slug":"ph-d-qualifying-exam-probability","status":"publish","type":"page","link":"https:\/\/sites.nd.edu\/bei-hu\/courses\/ph-d-qualifying-exam-probability\/","title":{"rendered":"Ph.D. Qualifying Exam &#8211; Prob"},"content":{"rendered":"\n<H2>    \nApplied Probability (ACMS 60850): Qualifying Exam Guide<br>\n<font size=\"3\">\n \n<\/font>\n<\/H2>    \n\n<p><b> Textbook:<\/b> &#8220;Probability and Random Processes&#8221; by Grimmett and Stirzaker,\nThird edition 2009, Oxford Univ Press,  ISBN 978-019-857222-0; Or fourth edition 2020, ISBN 978-019-8847595.\n\n\n<p><b> Syllabus:<\/b>\nYou are responsible for these materials:\n\n<ol>\n<li> Basic setup of probability theory (including sample spaces, conditional\nprobability, independence). Random variables (including the elements of\nmeasure and integration theory).\n<li> Discrete random variables (including random walks).\n<li> Continuous random variables, the basic distributions, sums of random variables.\n<li> Generating functions, branching processes, basic theory of characteristic\nfunctions.\n<li> Laws of large numbers, central limit theorems.\n<li> Markov chains (birth and death processes, Poisson processes)\n<li> Convergence of random variables (convergence in distribution, probability, mean-square, almost surely).\n<li>  Various stochastic processes, including Brownian motion,  and applications.\n<li> Martingales (discrete version only), including stopping times.\n<li> The rudiments of stochastic integration (including Ito&#8217;s formula).\n<\/ol>   \n \n<p>    \n<b>Here are sample exams:<\/b>\n\n<ol>\n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2026\/08\/Prob2026.pdf\">2026 Qualifying Exam in Probability (3 hours)<\/a>. \n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2026\/08\/Prob2026ans.pdf\">2026 Qualifying Exam answers (DO NOT read until you try the exam first)<\/a>. \n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Q-Prob2020.pdf\">2020 Qualifying Exam in Probability (3 hours)<\/a>. \n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Q-Prob2020-ans.pdf\">2020 Qualifying Exam answers (DO NOT read until you try the exam first)<\/a>. \n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Q-Prob2018.pdf\">2018 Qualifying Exam in Probability (3 hours)<\/a>. \n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Q-Prob2018-ans.pdf\">2018 Qualifying Exam answers (DO NOT read until you try the exam first)<\/a>. \n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Q-Prob2017.pdf\">2017 Qualifying Exam in Probability (3 hours)<\/a>. \n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Q-Prob2017-ans.pdf\">2017 Qualifying Exam answers (DO NOT read until you try the exam first)<\/a>. \n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Midterm.pdf\">A sample midterm exam (75 minutes)<\/a>.\n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Midterm-ans.pdf\">Midterm exam answers<\/a>.\n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Final16.pdf\">A sample final exam (2016 Fall take home)<\/a>.\n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Final16-ans.pdf\"> Final exam answers (2016 Fall take home)<\/a>.\n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Final17.pdf\">A sample final exam (2017 Fall take home)<\/a>.\n<li> <a href=\"http:\/\/sites.nd.edu\/bei-hu\/files\/2025\/02\/Final17-ans.pdf\"> Final exam answers (2017 Fall take home)<\/a>.\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Applied Probability (ACMS 60850): Qualifying Exam Guide Textbook: &#8220;Probability and Random Processes&#8221; by Grimmett and Stirzaker, Third edition 2009, Oxford Univ Press, ISBN 978-019-857222-0; Or fourth edition 2020, ISBN 978-019-8847595. Syllabus: You are responsible for these materials: Basic setup of probability theory (including sample spaces, conditional probability, independence). Random variables (including the elements of measure [&hellip;]<\/p>\n","protected":false},"author":2686,"featured_media":0,"parent":281,"menu_order":5,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-312","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/pages\/312","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/users\/2686"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/comments?post=312"}],"version-history":[{"count":16,"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/pages\/312\/revisions"}],"predecessor-version":[{"id":401,"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/pages\/312\/revisions\/401"}],"up":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/pages\/281"}],"wp:attachment":[{"href":"https:\/\/sites.nd.edu\/bei-hu\/wp-json\/wp\/v2\/media?parent=312"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}