NMAK13009U  Inference in Hidden Markov Models

Volume 2013/2014
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.
Stok and Stat 1+2
2 + 2 hours of lectures and 2 hours of exercise sessions per week for 7 weeks.
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.

  • Category
  • Hours
  • Lectures
  • 28
  • Practical exercises
  • 7
  • Preparation
  • 140
  • Exam
  • 24
  • Theory exercises
  • 7
  • Total
  • 206