This course prepares students to a rigorous study of Stochastic Differential Equations, as done in Math236. Towards this goal, we cover elements from the material of Stat310/Math230 sequence, emphasizing the applications to stochastic processes, instead of detailing proofs of theorems (see also comparison with Stat217/218 and Stat310/Math230).

Main topics are introduction to measurable, Lp and Hilbert spaces, random variables and (conditional) expectation, uniform integrability and modes of convergence, stationarity and sample path continuity of stochastic processes, examples such as Markov chains, Branching, Gaussian and Poisson Processes, Martingales and basic properties of Brownian motion.

** Prerequisites: ** Students should be comfortable with
probability at the level of Stat116/Math105/Math151
(summary of material)
and with real analysis at the level of Math115
(syllabus).
Past exposure to stochastic processes is highly recommended.

** Text: ** Download the
course lecture notes
and
** read ** each section of the notes prior to corresponding
lecture (see schedule). When doing so, you may skip items excluded from the
material for exams (see below) or marked as ``omit at first reading''
and all ``proofs''.
Kevin Ross short notes on
continuity of processes,
the martingale property,
and
Markov processes may help you in mastering these topics.

** Supplementary material: ** (texts on reserve at math. library)

- Rosenthal, A first look at rigorous probability theory (accessible yet rigorous, with complete proofs, but restricted to discrete time stochastic processes).
- Grimmett and Stirzaker, Probability and Random Processes (with most of our material, in a friendly proof oriented style).
- Shreve, Stochastic Calculus for Finance II: Continuous time models, Ch. 1,2,3,A,B (covering same material as the course, but more closely oriented towards stochastic calculus).
- Karlin and Taylor, A first course in Stochastic Processes, Ch. 6,7,8 (gives many examples and applications of Martingales, Brownian Motion and Branching Processes).
- Lawler, Stochastic Processes (more modern examples and applications than in Karlin and Taylor).

** Meeting: ** 380-380Y, MWF 12:15-1:05.

** Instructor 1: **
Amir Dembo,
Sequoia 129, F 1:15-2:30, lecture/office-hours, on Oct 19 - Nov 24;
or e-mail
amir at stat.stanford.edu
(please include MATH136 in your email title).

** Instructor 2: **
Tianyi Zheng,
380-382H, F 3:10-5:10, lecture/office-hours relevant on
Sep 23 - Oct 18, Dec 1 - Dec 9.
or e-mail
tzheng2 at math.stanford.edu
(please include MATH136 in your email title).

** CA1 (grading HW1/HW3/HW5/HW7/HW9) **: Li-Cheng Tsai,
380-380H, M 2:00-4:00 and W 11:00-12:00, Sep 30 - Dec 9,
or
e-mail lctsai at math.stanford.edu
(please include MATH136 in your email title).

** CA2 (grading HW2/HW4/HW6/HW8/HW9) **: Ruojun Huang, Sequoia 241,
Tu 10:30-12:00 and Tu 10:30-12:00.
or
email hruojun at stanford.edu
(please include MATH136 in your email title).

** Grading **:
Judgement based on Final (50%) and Midterm (25%) exam marks and
on consistent Homework efforts (25%).
At least 60% required for CR grade.

** Midterm:** Friday, 11/1, 12:10-1:10,
alternate seating in room 370-370,
exam's solution .
Three pages of notes (2 sides each) allowed,
handwritten or computer generated,
at any font readable without artificial magnification.
Material: Sections 1.1-3.3 of lecture notes,
except: from Section 1.4: uniform integrability;
all of Section 2.2; from Section 2.4: up to 2.4.3;
from Section 3.1: the cylindrical sigma-field.
practice exam and its
solution.

**
Final:
**
Monday, 12/9, 8:30-11:30,alternate seating in McCullough 115,
exam's solution .
Six pages of notes (2 sides each) allowed,
handwritten or computer generated,
at any font readable without artificial magnification.

** Material: ** Everything in lecture notes, except:
all of Section 2.2; from Section 2.4: up to 2.4.3; Section 4.1.2;
all of Section 6.3; everything marked as ``omit at first reading''
and all ``proofs'' unless done during lectures (80% of exam shall
be from Sections 4.1--6.2).

** Study tools:
List of key items,
**
Exercises 4.3.20, 4.4.6, 4.5.4, 4.6.7, 5.1.8, 5.2.6, 5.3.9,
6.1.19 and 6.2.12 are from previous finals.
See also
practice exam and its
solution.

** Homework of 2013: **
Problems from the
text
as listed on
HW1--HW9, are due 12:15 p.m. on a weekly basis (see dates below).
Collaboration allowed in solving the problems, but you are to provide
your own independently written solution.
Please deliver your assignment to class on due date (late homework
solutions will not be graded). Homework solutions posted on Fridays
and graded assignments returned in class on the following Wednesday
(HW9 returned in class 12/6).
HW1 due Wednesday 10/2,
solution;
HW2 due Wednesday 10/9,
solution;
HW3 due Wednesday 10/16,
solution;
HW4 due Wednesday 10/23,
solution;
HW5 due Wednesday 10/30,
solution;
HW6 due Wednesday 11/6,
solution;
HW7 due Wednesday 11/13,
solution;
HW8 due Wednesday 11/20,
solution;
HW9 due Wednesday 12/4,
solution.
**All solutions posted!**,

** Schedule ** (Read corresponding sections of notes before class):

9/23 M(1.1) W(1.2.1/1.2.2) F(1.2.2/1.2.3) 9/30 M(1.3.1/1.4.1) W(1.4.1/1.3.2) F(1.4.2) 10/7 M(1.4.2/1.4.3) W(2.1.1/2.1.2) F(2.3) 10/14 M(3.1) W(3.1/3.2.1) F(3.2.2) 10/21 M(3.2.3/3.3) W(5.1) F(4.1.1) 10/28 M(4.1.3/2.4) W(2.4/Review:1-3) F(Midterm) 11/4 M(4.2) W(4.3.1) F(4.3.1/4.3.2) 11/11 M(5.2/4.4.1) W(4.4.1/4.4.2) F(4.5/4.6) 11/18 M(5.3) W(6.1) F(6.1) 11/25 M(---) W(---) F(---) 12/2 M(6.2) W(6.2;Review:4-6) F(Review+Q/A)

** Approximately equivalent material (outdated):
**

- Grimmett and Stirzaker: 1.1-1.3, 1.5-1.6, 2.1, 2.3-2.5, 3.1-3.3, 3.5, 3.7, 4.1-4.6 (partially), 5.4, 5.6-5.9, most of 7.1-7.3 (10), 8.1, 8.2, 8.5, 8.6, 9.1 (=midterm), 9.6, 7.7-7.10, 12.1, 12.3-12.8, 13.4 (without most proofs).
- Shreve's book: 1.1-1.5, 2.1-2.6.

** List of key items: **

- Probability spaces, generated and Borel sigma-algebras. Indicators, simple functions, random variables. Expectation: Lebesgue and Riemann integrals, motonicity and linearity. Jensen's and Markov's inequalities. L_q spaces. Independence. Distribution, density and characteristic function. Convergence almost surely, in probability, in q-mean and in distribution/law (=weakly). Uniform integrability, Dominated and Monotone convergence. Conditional expectation: definition and properties.
- Stochastic processes: definition, stationarity, finite-dimensional distributions, version and modification, sample path continuity, right-continuous with left-limits processes. Kolmogorov's continuity theorem and Holder continuity. Stopping times, stopped sigma-fields and processes. Right-continuous and canonical filtrations, adapted and previsible processes.
- Examples: random walk; Gaussian distribution: for variables, vectors and processes, non-degeneracy, stationarity, closeness under 2-mean convergence.
- Brownian motion: definition, Gaussian construction, independence of increments, scaling and time inversion, Levy's martingale characterization, reflection principle, law of its maximum in an interval and first hitting time of positive levels, modulus of continuity, quadratic and total variation. Related processes: Geometric Brownian motion, Brownian bridge and Ornstein-Uhlenbeck process.
- Poisson distribution, approximation, and process: definition, rate, construction, independence of increments, memoryless property of the Exponential law, the dual process of independent Exponential interarrivals, the order statistics of independent uniform samples.
- Markov chain and process: Markov and strong Markov property, examples.
- Discrete and continous time martingales: definition, superMG and subMG, convex functions of, stopped MG and the martingale transform, existence of RCLL modification, Doob's optional stopping, representation, inequalities and convergence theorems, examples - Doob's martingale and martingales derived from random walk, Brownian motion, branching and Poisson processes.