NSCPHD1194 Inference in Hidden Markov Models
MSc programme in Statistics
MSc programme in Actuarial Mathematics
MSc programme in Mathematics-Economics
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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.
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.
- Category
- Hours
- Exam
- 27
- Lectures
- 28
- Practical exercises
- 7
- Preparation
- 137
- Theory exercises
- 7
- Total
- 206
- Credit
- 7,5 ECTS
- Type of assessment
- Written assignment, 27 hoursWritten take-home assignment (handing-out at 9 am and submission at 12 noon the following day).
- Exam registration requirements
To pass the course, the student has to present exercises in class at one of the exercise sessions.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Re-exam
Same as ordinary exam. If the requirement of presenting exercises in class during the course is not fulfilled, a written assignment is due two weeks before the re-exam. The assignment will be handed out three weeks before the re-exam.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NSCPHD1194
- Credit
- 7,5 ECTS
- Level
- Ph.D.
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- C
- Course capacity
- No limit
- Continuing and further education
- Study board
- Natural Sciences PhD Committee
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Susanne Ditlevsen (7-7a7c7a6875756c4774687b6f35727c356b72)
Lecturers
Susanne Ditlevsen