Ma140B - Probability (Winter 2025)

InstructorYassine El Maazouz
Course webpage http://www.yelmaazouz.org/Ma140B/index.html
Course code Ma/ACM/IDS 140 abc
LecturesMWF 14:00–14:55, 187 Linde Hall
E-Mail:maazouz [at] caltech [dot] edu
Office308 Linde Hall of Mathematics and Physics
Office HoursW 15:05–16:00
Excluding holidays. Please email me if you cannot make any of these times.
PrerequisitesMa108ABC and Ma140A are strongly recommended.
Required Text There is no required text for this course. However, the following will be used as references:
  1. [D] Durrett, Rick. Probability: Theory and Examples. Cambridge Series in Statistical and Probabilistic Mathematics, 49. Cambridge University Press, Cambridge, 2019.
  2. [K] Kallenberg, Olav. Foundations of Modern Probability. Springer, 2002.
  3. [LP] Levin, David and Peres, Yuval. Markov chains and mixing times. American Mathematical Society, Providence, RI, 2017.
  4. [T] Tamuz, Omer. Random Walks. Lecture notes (available online here).
Catalog Description This course sequence begins with an overview of measure theory, followed by topics that include random walks, the strong law of large numbers, the central limit theorem, martingales, Markov chains, characteristic functions, Poisson processes, and Brownian motion. Towards the end, some further topics may be covered, such as stochastic calculus, stochastic differential equations, Gaussian processes, random graphs, Markov chain mixing, random matrix theory, and interacting particle systems.
Syllabus I plan to cover the following topics (depending on how much progress we make):
  1. Markov chains.
  2. Random walks.
  3. Markov, Poisson and jump processes.
  4. Brownian motion.
  5. Lévy processes.
The schedule of the course will announced below and continuously updated as the course develops.
Homework There will be weekly problem sets. Collaboration (between students) on homework is encouraged. It is important to make sure you understand the solutions yourself, so you are required to write your own solutions separately. I also strongly recommend that you spend some time attempting the problems yourself first before discussing with someone else. You are required to name all your collaborators and sources of information you used for each assignment; any such named source may be used. You can also use any result discussed in the lectures. DO NOT PLAGIARIZE!
Please write clear and legible solutions. It is strongly recomemded to write your solutions in TeX. Homework is due on Fridays at 6pm and is to be submitted on Gradescope. Late homework submissions will not be accepted.
Final There will be a take-home final due on Monday March 17th at 6pm, to be submitted on Gradescope. Late submissions will not be accepted. You are not allowed to collaborate with anyone on the take-home final.
Grading The final grade will be based 70% on weekly problem sets and 30% on the take-home final. The lowest homework grade will be dropped.
Teaching AssistantEthan Davis

Schedule

DateCoverd Material Problem Sets
Mo. Jan. 06 2025 Quick review of conditional expectation and martingales in discrete time + some examples
We. Jan. 08 2025 Class is cancelled due to wildfires.
Fr. Jan. 10 2025 Class is cancelled due to wildfires.
 
Mo. Jan. 13 2025 Upcrossing inequality and almost sure convergence of martingales. HW1
We. Jan. 15 2025 Doob's maximal inequaliy and $L^p$ convergence.
Fr. Jan. 17 2025 Uniform integrability and $L^1$-convergence.
 
Mo. Jan. 20 2025 MLK Jr Day. HW2
We. Jan. 22 2025 Optional stopping theorems.
Fr. Jan. 24 2025 Class cancelled.
 
Mo. Jan. 27 2025 Construction of Markov chains. HW3
We. Jan. 29 2025 The Markov property.
Fr. Jan. 31 2025 Classification of states.
 
Mo. Feb. 03 2025 Invariant measures 1 HW4
We. Feb. 05 2025 Invariant measures 2
Fr. Feb. 07 2025 Asymptotic behaviour of Markov chains 1
 
Mo. Feb. 10 2025 Asymptotic behaviour of Markov chains 2 HW5
We. Feb. 12 2025 Definition and construction of Brownian motion
Fr. Feb. 14 2025 First properties of Brownian motion
 
Mo. Feb. 17 2025 President's day. HW6
We. Feb. 19 2025 Brownian motion path properties 1
Fr. Feb. 21 2025 Brownian motion path properties 2.
 
Mo. Feb. 24 2025 Simple Markov property HW7
We. Feb. 26 2025 Stopping times and right-continuous filtrations
Fr. Feb. 28 2025 Hitting times of Brownian motion
 
Mo. Mar. 03 2025 Strong Markov property HW8
We. Mar. 05 2025 Reflexion principle
Fr. Mar. 07 2025 Markovian processes extracted from Brownian motion
 
Mo. Mar. 10 2025 Martingales HW9
We. Mar. 12 2025
   
Mo. Mar. 17 2025 FINAL