|Prof:||Robert L. Wolpertfirstname.lastname@example.org||OH: Mon 1:30–3, 211C Old Chem|
|TAs:||Felipe Ossaemail@example.com||OH: Mon 6–7:30, 203B Old Chem|
|&||Andy McCormackfirstname.lastname@example.org||OH: Wed 4–5:30, 203B Old Chem|
|&||Azeem Zamanemail@example.com||OH: Tue 12–1:30, 203B Old Chem|
|Class:||Tue/Thu 1:25–2:40pm, D-106 LSRC|
|Text:||Sidney Resnick,||A Probability Path (pdf)||Additional references|
|Opt'l:||Patrick Billingsley,||Probability and Measure||(a classic)|
|Jeff Rosenthal,||A First Look at Rigorous Probability Theory (2/e)||(easier than Resnick, and just $33)|
|Jacod & Protter,||Probability Essentials (2/e)||(easier than Resnick, $53)|
|Rick Durrett,||Probability Theory & Examples (5/e)||(more complete, free on-line)|
|I. Foundations of Probability||Problems||Due|
|Aug 27-29||Probability spaces: Sets, Events, and σ-Fields||hw1||Sep 05|
|Sep 03-05||Construction & extension of Measures||hw2||Sep 12|
|Sep 10-12||Random variables and their Distributions||hw3||Sep 19|
|Sep 17-19||Expectation & the Lebesgue Theorems||hw4||Sep 26|
|Sep 24-26||Expect'n Ineqs, Lp Spaces, & Independence||hw5||Oct 01|
|Oct 01-03||Review & in-class Midterm Exam I ('16, '17, '18)||Hists:||Exam, Course|
|--- Fall Break (Oct 07-09) ---|
|Oct -10||Zero-one Laws & Hoeffding's Inequality||hw6||Oct 17|
|II. Convergence of Random Variables and Distributions|
|Oct 15-17||Convergence: a.s., pr., Lp, L∞, UI.||hw7||Oct 24|
|Oct 22-24||Laws of Large Numbers, Strong & Weak||hw8||Oct 31|
|Oct 29-31||Fourier Theory and the Central Limit Theorem||hw9||Nov 07|
|III. Conditional Probability & Conditional Expectations|
|Nov 05-07||Cond'l Expectations & the Radon-Nikodym thm||hw10||Nov 12|
|Nov 12-14||Review & in-class Midterm Exam II ('16, '17, '18)||Hists:||Exam, Course|
|Nov 19-21||Intro to Martingales (a, b) & Extremes||hw11||Dec 10|
|--- TG Recess + Grad Reading Pd (Nov 27-Dec 10) ---|
|Dec 12||Possible Final Exam Review Session: Loc TBD, 1:25-2:40|
|Dec 14||2:00-5:00pm Sat: In-class Final Exam ('16, '17, '18)||Hists:||Exam, Course.|
Students are expected to be well-versed in real analysis at the level of W. Rudin's Principles of Mathematical Analysis or M. Reed's Fundamental Ideas of Analysis— the topology of Rn, convergence in metric spaces (especially uniform convergence of functions on Rn), infinite series, countable and uncountable sets, compactness and convexity, and so forth. Try to answer the questions on this analysis diagnostic quiz to see if you're prepared. Most students who majored in mathematics as undergraduates will be familiar with this material, but students with less background in math should consider taking Duke's MTH 531, Basic Analysis I (somewhat more advanced than MTH 431, Advanced Calculus I, but that's a good second choice) before taking this course. It is also possible to learn the background material by working through one of the standard texts (like Rudin's or Reed's books listed above) and doing most of the problems, preferably in collaboration with a couple of other students and with a faculty member (maybe me) to help out now and then. More advanced mathematical topics from real analysis, including parts of measure theory, Fourier and functional analysis, are introduced as needed to support a deep understanding of probability and its applications. Topics of later interest in statistics (e.g., regular conditional density functions) are given special attention, while those of lesser statistical interest may be omitted.
Most students in the class will be familiar with undergraduate-level probability at the level of STA 230 = MTH 230 or MTH 340. Students should be or become familiar with the usual commonly occurring probability distributions (here is a list of many of them).
Most weeks will have lecture notes available (click on the "Week" column above if it's blue or green). This is syllabus is tentative, last revised , and will almost surely be superseded— reload your browser for the current version. Warning: lecture notes and homework problems are also revised from time to time as I try to improve them. SO, it's best not to print out future problem sets or lecture notes until you need them. Lecture notes all have a "last edited" date at the end; homeworks might change any time until the week before they are due.
Homework problems are awarded points (usually up to three, but sometimes four or five) based on your success in communicating a correct solution. For full credit the solution must be clear, concise, and correct; even a correct solution will lose points or be returned ungraded if it is not clear and concise. Neatness counts. Consider using LaTeX and submitting your work in pdf form if necessary (it's good practice anyway). Avoid using proof-by-contradiction wherever possible. Homeworks may be submitted in paper form (just bring them to class on Thursday) or electronically in the form of pdf files (submit as Sakai "Assignments" or send by e-mail to the course TAs, Felipe, Andy and Azeem). Most homeworks are collected on Thursday at the start of class, and returned the following Tuesday. Homeworks assigned the week before midterm exams are collected on Tuesday instead of Thursday, so I can answer questions about them before the exam. Homeworks may be submitted electronically if necessary (for example, if you're out of town on the due date). Remember, homework problems are subject to change up to a week before the due-date.
In-class Midterm and Final examinations are closed-book and closed-notes with one 8½"×11" sheet of your own notes permitted. Tests from recent STA711 offerings are available to help you know what to expect and to help you prepare for this year's tests:
|Fall 2016:||1st Midterm||2nd Midterm||Final Exam|
|Fall 2017:||1st Midterm||2nd Midterm||Final Exam|
|Fall 2018:||1st Midterm||2nd Midterm||Final Exam|
You may discuss and collaborate in solving homework problems, but you may not copy— each student should write up his or her solution. Cheating on exams, copying or plagiarizing homeworks or projects, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, and will not be tolerated. They also violate Duke's Community Standard and will be referred to the Graduate School Judicial Board or the Dean of the Graduate School. Additionally, there may be penalties to your final course grade. Please review Duke's Standards of Conduct.