Computer-intensive Statistics

The course is included in the study program Mathematical Sciences - master. It may be taken independent of study program upon approval of the Department of mathematics and statistics. This course is also available for inbound exchange students.

The course covers stochastic sampling techniques, Monte Carlo simulation, bootstrapping, and optimization methods like the Expectation-Maximization (EM) algorithm and simulated annealing. The course also includes Bayesian inference, Markov chain Monte Carlo (MCMC) methods, and an introduction to the methodology of integrated nested Laplace approximations (INLA).

Bachelor of science degree in statistics/mathematics or equal.

Application code is 9371.

The course introduces key computational methods for solving statistical problems in both frequentist and Bayesian settings. It provides a solid theoretical foundation for these methods. Upon successful completion of the course, students will be able to

• Implement stochastic sampling techniques, perform Monte Carlo integration, and apply variance-reduction methods.
• Implement and apply bootstrap methods for statistical inference. This includes understanding the bootstrap principle and using bootstrapping to approximate sampling distributions, standard errors, confidence intervals, and perform hypothesis testing.
• Apply the EM-algorithm to maximize likelihood functions or posterior distributions, particularly in problems involving latent or missing data.
• Implement simulated annealing as a stochastic optimization algorithm, including its application to the travelling salesman problem.
• Implement MCMC methods, such as the Metropolis-Hastings algorithm and Gibbs sampling, and assess their convergence and performance.
• Explain the key principles of the INLA methodology and use it to perform approximate Bayesian inference for regression models within the class of latent Gaussian models.

Anbefalte forkunnskaper

STA-2001 Stochastic Processes, STA-2002 Theoretical Statistics

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

• S-320 Comutational statistics 9 ects
• STA-8001 Computer-intensive Statistics 10 ects

Language of instruction and examination

Schedule

This course is open to incoming exchange students.

Study level: Master’s

Admission prerequisites:This course has admission prerequisites, which are listed under the Admission requirements section. Please review this information carefully before adding the course to your Learning Agreement.

For details on how to apply for exchange, course selection guidelines, or to contact the Incoming Admissions Team, please visit: Admissions for Student Exchange.

Re-sit examination