This is the page from Fall 2016. I am teaching the course again in Fall 2016 and will update this page over the summer.
IMPORTANT. The best reference, and some of the homeworks, are from R. Durrett Probability: Theory and Examples 4th Edition.
Instructor: David Aldous
Teaching Assistant (GSI): Wenpin Tang.
Class time: TuTh 2.00 - 3.30 in room 70 Evans
Optional Lab section: F 1.00-2.00 in 330 Evans
This is the first half of a year course in mathematical probability at the measure-theoretic level. It is designed for students whose ultimate research will involve rigorous proofs in mathematical probability. It is aimed at Ph.D. students in the Statistics and Mathematics Depts, but is also taken by Ph.D. students in Computer Science, Electrical Engineering, Business and Economics who expect their thesis work to involve probability.
In brief, the course will cover
|1/2||Aug 28, Sep 2/4||Fields, sigma-fields, measurable functions, measures, Lebesgue measure, distribution functions, coin-tossing, abstract integration||2,10,13; 3,12,15||1.1, 1.2|
|3||Sep 9/11||Probability spaces, random variables, expectation, inequalities||4,5,20||1.3 - 1.6|
|4||Sep 16/18||Independence, WLLN, Bernstein's theorem, Borel-Cantelli lemmas, 4'th moment SLLN, Glivenko-Cantelli, gambling on favorable game.||6,20||2.1, 2.2|
|5||Sep 23/25||a.s. limit theorems for maxima, 2nd moment SLLN, modes of convergence, dominated convergence, maximal inequality, convergence of random series, 1st moment SLLN||6,21||2.3, 2.4|
|6||Sep 30/Oct 2||variant SLLNs, Fatou, Renewal SLLN. Stopping times, Wald's equation, Kolmogorov 0-1 law; Radon-Nikodym; Cantor measure, decomposition of measures on R.||22||2.4, 2.5|
|7||Oct 7||Large deviations||22, 9||4.1, 2.6|
|7/8||Oct 9/14/16||joint distributions correspond to marginals and a kernel. Product measure, Fubini's theorem and examples. Kolmogorov consistency theorem.||32, 33||5.1, A4|
|9||Oct 21/23||Conditional expectation. Definition and examples of martingales. Convexity.||18, 34, 36||1.7, 5.1|
|10||Oct 28/30||Doob decomposition, martingale transforms, stopping times, bounded version of Optional Sampling Theorem. Maximal and upcrossing inequalities.||35||5.2|
|11||Nov 4/6||MG convergence theorems, Levy 0-1 law, L_p convergence, conditional Borel-Cantelli, Kakutani's theorem, general form of optional sampling, MG analog of Wald.||35||5.3, 5.7|
|12/13||Nov 13/18/20||Boundary crossing examples. Patterns in coin-tossing. MG proof of Radon-Nikodym theorem. Azuma's inequality; examples. Reversed MGs and SLLN. Exchangeability and de Finetti's theorem. Kolmogorov consistency theorem.||35||5.3, 5.4, 5.5, 5.7|
|14/15||Nov 25, Dec 2/4||Brownian motion. Existence and path continuity. Invariance properties. Path non-differentiability. Associated martingales and their use in finding distributions, e.g. of hitting time for BM with drift. Reflection principle and formulas derived from it. Mention bridge, excursion, meander.||37||8.1 - 8.5|
|Take-home final. 2.00pm Thursday 12/4 -- 2.00pm Monday 12/8|
P. Billingsley Probability and Measure (3rd Edition). Chapters 1-30 contain a more careful and detailed treatment of some of the topics of this semester, in particular the measure-theory background. Recommended for students who have not done measure theory.
R. Leadbetter et al A Basic Course in Measure and Probability: Theory for Applications is a new book giving a careful treatment of the measure-theory background.
There are many other books at roughly the same ``first year graduate" level. Here are my personal comments on some.
D. Khoshnevisan Probability is a well-written concise account of the key topics in 205AB.
K.L. Chung A Course in Probability Theory covers many of the topics of 205A: more leisurely than Durrett and more focused than Billingsley.
D. Williams Probability with Martingales has a uniquely enthusiastic style; concise treatment emphasizes usefulness of martingales.
Y.S. Chow and H. Teicher Probability Theory: Independence, Interchangeability, Martingales . Uninspired exposition, but has useful variations on technical topics such as inequalities for sums and for martingales.
R.M. Dudley Real Analysis and Probability. Best account of the functional analysis and metric space background relevant for research in theoretical probability.
B. Fristedt and L. Gray A Modern Approach to Probability Theory. 700 pages allow coverage of broad range of topics in probability and stochastic processes.
L. Breiman Probability. Classical; concise and broad coverage.
O. Kallenberg Foundations of Modern Probability. Quoting an amazon.com reviewer: ``.... a compendium of all the relevant results of probability ..... similar in breadth and depth to Loeve's classical text of the mid 70's. It is not suited as a textbook, as it lacks the many examples that are needed to absorb the theory at a first pass. It works best as a reference book or a "second pass" textbook."
John B. Walsh Knowing the Odds: An Introduction to Probability. New in 2012. Looks very nice -- concise treatment with quite challenging exercises developing part of theory.
George Roussas An Introduction to Measure-Theoretic Probability. Recent treatment of classical content.
Santosh Venkatesh The Theory of Probability: Explorations and Applications. Unique new book, intertwining a broad range of undergraduate and graduate-level topics for an applied audience.
I. Florescu Probability and Stochastic Processes. Very clearly written, and with 550 pages gives a broad coverage of topics including intro to SDEs.
Jim Pitman has his very useful lecture notes linked to the Durrett text; these notes cover more ground than my course will! Also some lecture notes by Amir Dembo for the Stanford courses equivalent to our 205AB.
Grading 60% homework, 40% final.
Wenpin Tang (firstname.lastname@example.org) Mondays 3.30 - 5.00 in 447 Evans. TBA
if you email us put "STAT 205A" in subject.