Spring
semester 2015
02433 Hidden Markov Models 5 ects point
|
Course
overview
Prerequisites:
02417 – Time series analysis
Optional:
02407 – Stochastic processes
Textbook:
Hidden
Markov
Models
for
Time
Series
–
W.
Zucchini
&
I.
L.
MacDonald, 2009
R source code from appendix
and data sets.
Week
1: Supplementary
slides and solutions
for selected exercises.
Week 2: Supplementary
slides and solutions
for selected exercises.
Week
3: Supplementary
slides and solutions
for selected exercises. Written
exercise
1.
Week
4: Supplementary
slides and solutions
for selected exercises.
Week
5: Supplementary
slides and solutions
for selected exercises.
Week
6: Supplementary
slides and solutions
for selected exercises.
Week 7:
Notes on state-space
modelling with HMMs. Written
exercise 2. Data 1
- Data
2.
Week 8: Supplementary
slides and solutions
for selected exercises.
Week 9: Notes on DNA copy No.
data.
Week
10:
Example on wind power
analysis and forecasting. Winddata
for exercise. Solution to
exercise.
Week 11: Wind direction at Koeberg (Chapter 12). Written
exercise
3, wind
power
data.
Week 12: Example
on animal behaviour with feedback (Chapter 16).
Cursory reading: Altman,
R.M. (2007). Mixed hidden Markov models: An extension of the hidden
Markov model to the longitudinal data setting. JASA, 102, 201-210.
Week 13:
Example on fish tracking using HMM.
For
more information contact:
Jan Kloppenborg Møller
–
jkmo@dtu.dk