Computational statistics is a branch of mathematical sciences concerned with efficient methods for obtaining numerical solutions to statistically formulated problems. This course will introduce students to a variety of computationally intensive statistical techniques and the role of computation as a tool of discovery. Topics include numerical optimization in statistical inference [expectation-maximization (EM) algorithm, Fisher scoring, etc.], random number generation, Monte Carlo methods, randomization methods, jackknife methods, bootstrap methods, tools for identification of structure in data, estimation of functions (orthogonal polynomials, splines, etc.), and graphical methods. Additional topics may vary. Coursework will include computer assignments.
Multivariate calculus, familiarity with basic matrix algebra and EN.625.603 Statistical Methods and Data Analysis.
Provide a background in the computationally intensive tools and methodologies relevant to statistical analysis and the visualization of complex data.
- Introduce and understand modern computational methods used in statistics. Included are methods for simulation, estimation and visualization of statistical data.
Understand the role of computation as a tool of discovery in data analysis.
- Be able to appropriately apply computational methodologies to real world statistical problems.
When This Course is Typically Offered
The course is offered every spring and fall online.
- Fisher Scoring
- EM Algorithm
- Random Number Generation
- Monte Carlo Methods
- Jackknife Methods
- Bootstrap Methods
- Kernel Estimation
- Bivariate Smoothing
- Viewing Data
Student Assessment Criteria
Computer and Technical Requirements
This course is on computational methods and many of the assignments will require the use of a computer. An introduction to the statistical programming language R will be presented as part of the course and students will be required to complete their assignments in R.
Homework will be assigned throughout the semester. No late homework will be accepeted without prior permission from the instructor. Additionally, students will be expected to participate in discussion boards throughout the semeseter, but will not be required to contribute weekly.
Textbook information for this course is available online through the MBS Direct Virtual Bookstore.
There are notes for this course.
Term Specific Course Website
(Last Modified: 12/16/2019 03:29:33 PM)