Spring 2022 - STAT 853 G100
Applications of Statistical Computing (4)
Class Number: 6736
Delivery Method: In Person
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.
- 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.
- Assignments 40%
- Participation/Attendance 20%
- Project 40%
Above grading is subject to change
Numerical Analysis for Statisticians, 2nd ed. Author: Kenneth Lange. Publisher: Springer
Monte Carlo Statistical Methods, 2nd ed. Authors: Christian Robert and George Casella. Publisher: Springer
Introducing Monte Carlo Methods with R. Authors: Christian Robert and George Casella. Publisher: Springer
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.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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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 (firstname.lastname@example.org or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.