辅导案例-PSTAT 160A
PSTAT 160A Applied Stochastic Processes Spring 2020
(This version: 3/30/20)
Instructor: Zhijian Li (zhijian@pstat.ucsb.edu)
Lectures: Tuesday & Thursday 11:00 p.m. – 12:15 p.m. in Zoom
https://ucsb.zoom.us/j/498424882?pwd=bmdnSG5wbGFibDBqei9yK2pSZnRGdz09
Password: 160160
Office Hours: Tuesday 10:00 a.m. – 10:55 a.m., Thursday 7:00 p.m. – 8:00 p.m. in Zoom
https://ucsb.zoom.us/j/697117750
Password: 160160
Course Webpage: See GauchoSpace and Piazza.
Teaching Assistants and Discussion Sections: See GauchoSpace and Piazza.
Prerequisites: Mathematics 4A or 4AI or 5A, Mathematics 8, and PSTAT 120A. A minimum letter
grade of C or better must be earned in each course.
Catalog Description: Discrete probability models. Review of discrete and continuous probability.
Conditional expectations. Simulation techniques for random variables. Discrete time stochastic
processes: random walks and Markov chains with applications to Monte Carlo simulation.
Textbooks:
1.) Robert P. Dobrow, Introduction to Stochastic Processes with R, John Wiley & Sons Inc.,
2016. (main reference)
2.) David F. Anderson, Timo Seppa¨la¨inen, Benedek Valko´, Introduction to Probability,
Cambridge University Press, 2018. (for review PSTAT 120A)
3.) David Stirzaker, Elementary Probability, 2nd edition, Cambridge University Press, 2003.
(Chapter 5.6 on Random Walk)
We will cover following topics (cf. also schedule below):
Chapter 1. Review of Discrete and Continuous Probability.
Chapter 2. Conditional Expectation.
Chapter 3. Moment Generating Functions.
Chapter 4. Tail Bounds & Limit Theorems.
Chapter 5. Random Walk.
Chapter 6. Markov Chains.
Chapter 7. Simulation and Markov Chain Monte Carlo.
Homework: There will be 7 Homework assignments. Homework problems will be posted every
Friday (starting from April 3rd) on Gauchospace and will be submitted on Gauchospace on
Tuesday before the lecture (due 11 a.m.) two weeks later. No late homework submission
will be accepted! Two problems will be graded by your TA. Graded homework will be discussed
during the sections. Homework will count for final grade (see below). The lowest homework grade
will be dropped.
Python Homework: There will be 6 Python Homework assignments. Python problem sheets in the
format of Jupyter Notebooks will be posted every Friday (starting from April 3rd) on Gauchospace
and will be due on Friday two weeks later at 11:59 p.m. They will be submitted via GauchoSpace.
Please submit your pdf file and your jupyter notebook file (.ipynb) with all coding and
results. Please give yourself enough time to submit your work. No late homework
submission will be accepted! Python homework will count for final grade (see below). The
lowest Python homework grade will be dropped.
Group Work Policy: You are strongly encouraged to discuss the homework problems and Python
programming problems together with your classmates on Piazza but you have to submit your own
work! Exact copies will all be graded with 0 points!
Exams:
Midterm Exam: Thursday, May 7, during normal lecture hours. You will download the midterm
on Gauchospace and then submit it on Gauchospace.
Final Exam: A complicated and difficult Take home final. More details will be given at
the end of this quarter.
You may need to open your camera during the midterm. One sided handwritten note may be allowed
in the midterm. Students will not be allowed to take makeup exams.
Grading: Your cumulative average will be based on whichever of the following two weighted averages
is better.
Scheme 1 Weight
Python Homework 15%
Homework 15%
Midterm 30%
Final Exam 40%
Scheme 2 Weight
Python Homework 15%
Homework 15%
Final Exam 70%
Your course grade will be determined by your cumulative average at the end of the term and will be
based on the following scale:
Release Monday, 6/8/2020 12PM. Submit by 6/10/2020 11:59 PM
Grade Percentage in Course
A 100− 93.00
A− 92.99− 90.00
B+ 89.99− 87.00
B 86.99− 83.00
B− 82.99− 80.00
C+ 79.99− 77.00
C 76.99− 73.00
C− 72.99− 70.00
D+ 69.99− 67.00
D 66.99− 63.00
D− 62.99− 60.00
F 59.99− 0
Academic Dishonesty: Academic dishonesty is considered a serious offense at UCSB. Students
caught cheating shall be subject to the sanctions and other remedies described in UCSB’s Academic
Misconduct Policy and Procedures. It is in your best interest to maintain your academic integrity!
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