NMAK13009U Inference in Hidden Markov Models
Volume 2013/2014
Education
MSc programme in Statistics
MSc programme in Actuarial Mathematics
MSc programme in Mathematics-Economics
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.
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.
Academic qualifications
Stok and Stat
1+2
Teaching and learning methods
2 + 2 hours of lectures and
2 hours of exercise sessions per week for 7 weeks.
Workload
- Category
- Hours
- Exam
- 24
- Lectures
- 28
- Practical exercises
- 7
- Preparation
- 140
- Theory exercises
- 7
- Total
- 206
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Continuing Education - click here!
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Written assignment, 24 timer---
- Exam registration requirements
- To pass the course, the student has to present exercises in class.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner.
- Re-exam
- Same as ordinary exam
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome.
Course information
- Language
- English
- Course code
- NMAK13009U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- B
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Susanne Ditlevsen (susanne@math.ku.dk)
Phone +45 35 35 07 85, office,
04.4.13
Saved on the
30-04-2013