Tosta II - part 2
In the last part of the course we will consider a) numerical methods for generalized linear mixed models and b) give an introduction to Markov random fields.
Some data sets and R-code used in the course.
- Week 41: Slides (updated 28.10.21). Videos tosta_logistic1-4 at youtube.
43: Slides. Note (corrected 04.11.21)
exercise on Laplace approximation.
We meet 8.15-10 Thursday October 28 in 2.120. Allocation of
exercises from Week 41: 5219b 1, 5222 2, 5213a 3, 5213b+5215a 4,
44: Slides (corrected 10.11.21). Practical
exercise (updated 30.10.21) on importance sampling.
We meet 8.15-10 Thursday November 4 in 5.227.
- Week 45 Slides on Markov
random fields I (updated 16.11.21). Additional reading: Chapter on conditional auto regressions (beware, their notation differs a bit from mine).
We meet 8.15-10 Thursday November 11 in 5.227.
- Week 46: Slides on Markov
random fields II (updated 26.11.21). We continue with Markov random fields.
We meet 8.15-10 Tuesday November 16 in 5.034 aud B.
- Week 47: selfstudy on Markov random fields and Bayesian image analysis.
We meet 8.15-10 Friday November 26 in 5.227.
- Week 48. We meet 8.15-10 Thursday December 2 in 5.227.
Additional reading material: page 1-23 in "Markov random fields and their applications" by Kindermann and Snell.
The curriculum for the course and exam consists of the lecture slides including the exercises given in the slides.
- Computation of the likelihood function for GLMMs: deterministic quadrature
- Computation of the likelihood function for GLMMs: Monte Carlo methods
- Markov random fields: the Hammersley-Clifford theorem
- Markov random fields: Brooks factorization and Gaussian Markov random fields