Computational Statistics, 6 credits

Course content

• Basic concepts in numerical analysis
• Numerical optimization, linear algebra and integration
• Random number generation
• Monte Carlo simulation and variance reduction
• Bootstrap and Jackknife
• MCMC methods

Course outcome

After completing the course the student shall have:

• have knowledge of numerical methods and their limitations (Written Examination, Assignments)
• have knowledge of common computationally intensive methods for statistical analysis (Written Examination, Assignments).
• be able to independently implement computational algorithms (Assignments).
• have the ability to independently adapt and select an appropriate algorithm based on the requirements of the statistical issue (Written Examination, Assignments)
• have the ability to independently seek new knowledge and judge its relevance for the statistical issue at hand (Assignments)
• have the ability to independently design simulation studies for evaluating the statistical properties of a test or estimator (Written Examination, Assignments).