Spring 2022 - STAT 853 G100

Applications of Statistical Computing (4)

Class Number: 6736

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo, We 10:30 AM – 12:20 PM
    AQ 5006, Burnaby

  • Prerequisites:

    STAT 830 or equivalent or permission of instructor.

Description

CALENDAR DESCRIPTION:

An introduction to computational methods in applied statistics. Topics can include: the bootstrap, Markov Chain Monte Carlo, EM algorithm, as well as optimization and matrix decompositions. Statistical applications will include frequentist and Bayesian model estimation, as well as inference for complex models. The theoretical motivation and application of computational methods will be addressed.

COURSE DETAILS:

Course Outline:

We will cover topics from among the following:
- Foundations: review of Bayesian inference, finite precision arithmetic, pseudorandom number generation, computational efficiency in matrix operations.
- Low-rank approximation of data.
- Optimization: gradient and gradient-free methods/
- Monte Carlo estimation: importance sampling, Markov chain Monte Carlo, sequential Monte Carlo methods.
- Other: EM algorithm, variational Bayes approximations to posterior distributions.

Grading

  • Assignments 40%
  • Participation/Attendance 20%
  • Project 40%

NOTES:

Above grading is subject to change

Materials

RECOMMENDED READING:

Numerical Analysis for Statisticians, 2nd ed. Author: Kenneth Lange. Publisher: Springer
ISBN: 9781441959447

Monte Carlo Statistical Methods, 2nd ed. Authors: Christian Robert and George Casella. Publisher: Springer
ISBN: 9780387212395

Introducing Monte Carlo Methods with R. Authors: Christian Robert and George Casella. Publisher: Springer
ISBN: 978-1-4419-1575-7

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN SPRING 2022

Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place.  Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.