401-4619-67L  Advanced Topics in Computational Statistics

SemesterAutumn Semester 2018
LecturersN. Meinshausen
Periodicitytwo-yearly recurring course
CourseDoes not take place this semester.
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
401-4619-00 VAdvanced Topics in Computational Statistics
Does not take place this semester.
2 hrsN. Meinshausen

Catalogue data

AbstractThis lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling.
ObjectiveStudents learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.
ContentThe main focus will be on graphical models in various forms:
Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models
Prerequisites / NoticeWe assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersN. Meinshausen
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationStudents must take the exam in Winter 2018 or in Summer 2018. Be aware that no exam will be offered afterwards until the course will be read again.
Written aidsNone
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Data Science MasterCore ElectivesWInformation
Doctoral Department of MathematicsGraduate SchoolWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Electrical Engineering and Information Technology MasterSpecialization CoursesWInformation
Mathematics MasterSelection: Probability Theory, StatisticsWInformation
Computational Science and Engineering MasterElectivesWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation