|Prof:||Robert L. Wolpertfirstname.lastname@example.org||OH: Mon 3:00-5:00pm, 211c Old Chem|
|TA:||Bai Liemail@example.com||OH: Tue 7:00-9:00pm, 025 Old Chem|
|Class:||Mon/Wed 1:25-2:40pm, 126 Soc Psych|
|Text:||Sidney Resnick,||A Probability Path||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, $33, free on-line)|
|Jacod & Protter,||Probability Essentials (2/e)||(easier than Resnick, $53)|
|Rick Durrett,||Probability Theory & Examples (4.1/e)||(more complete, free on-line)|
|I. Foundations of Probability||Problems||Due|
|Aug 28-30||Probability spaces: Sets, Events, and σ-Fields||hw1||Sep 06|
|Sep 04-06||Construction & extension of Measures||hw2||Sep 13|
|Sep 11-13||Random variables and their Distributions||hw3||Sep 20|
|Sep 18-20||Expectation & the Lebesgue Theorems||hw4||Sep 27|
|Sep 25-27||Expect'n Ineqs, Lp Spaces, & Independence||hw5||Oct 02|
|Oct 02-04||Review & in-class Midterm Exam I ('14, '15, '16)||Hists:||Exam, Course|
|--- Fall Break (Oct 07-10) ---|
|Oct -11||Zero-one Laws & Hoeffding's Inequality||hw6||Oct 18|
|II. Convergence of Random Variables and Distributions|
|Oct 16-18||Convergence: a.s., pr., Lp, Loo, UI.||hw7||Oct 25|
|Oct 23-25||Laws of Large Numbers, Strong & Weak||hw8||Nov 01|
|Oct 30-01||Fourier Theory and the Central Limit Theorem||hw9||Nov 08|
|III. Conditional Probability & Conditional Expectations|
|Nov 06-08||Cond'l Expectations & the Radon-Nikodym thm||hw10||Nov 13|
|Nov 13-15||Review & in-class Midterm Exam II ('14, '15, '16)||Hists:||Exam, Course|
|Nov 20-||Introduction to Martingales (a, b)||hw11||Nov 29|
|--- Thanksgiving Recess (Nov 22-26) ---|
|Nov 27-29||Heavy tails and Extreme Values & Final Review|
|Dec 16||7-10pm Sat: In-class Final Exam ('14, '15, '16)||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.
Homework problems are awarded zero to three points each, based on your success in communicating a correct solution. For the full three points 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 Wednesday) or electronically in the form of pdf files (submit as Sakai "Assignments" or send by e-mail to the course TA, firstname.lastname@example.org). Homeworks assigned the week before midterm exams are collected on Monday instead of Wednesday, so I can answer questions about them before the exam.
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 2014:||1st Midterm||2nd Midterm||Final Exam|
|Fall 2015:||1st Midterm||2nd Midterm||Final Exam|
|Fall 2016:||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.