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NMAK13009U  Inference in Hidden Markov Models Volume 2013/2014

Course information

LanguageEnglish
Credit7,5 ECTS
LevelFull Degree Master
Duration1 block
Placement
Block 2
Schedule
B
Course capacityNo limit
Continuing and further education
Study boardStudy Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Course responsible
  • Susanne Ditlevsen (7-75777563707067426f63766a306d7730666d)
Phone +45 35 35 07 85, office, 04.4.13
Saved on the 30-04-2013
Education
MSc programme in Statistics
MSc programme in Actuarial Mathematics
MSc programme in Mathematics-Economics
Content
Hidden Markov models: Definition and properties. Estimation by direct maximization of the likelihood, by the EM algorithm and further MC methods. Forecasting, decoding and prediction.
Learning Outcome
Knowledge:
The student should know what Hidden markov models are and know about different inference methods.
Skills:
The students shall be able to set up hidden Markov models, and obtain insight into accessible methods of parameter estimation, and apply them to relevant models.
Competences:
The student should be able to generalize from the specific models introduced in the course to specific problems encountered further on.
Teaching and learning methods
2 + 2 hours of lectures and 2 hours of exercise sessions per week for 7 weeks.
Academic qualifications
Stok and Stat 1+2
Sign up
Through STADS self service
Exam
Credit7,5 ECTS
Type of assessment
Written assignment, 24 timer
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Exam registration requirementsTo pass the course, the student has to present exercises in class.
AidAll aids allowed
Marking scale7-point grading scale
Censorship formNo external censorship
One internal examiner.
Re-examSame as ordinary exam
Criteria for exam assesment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome.

Workload
CategoryHours
Lectures28
Practical exercises7
Preparation140
Exam24
Theory exercises7
Total206
Saved on the 30-04-2013