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.

  1. Week 41: Slides (updated 28.10.21). Videos tosta_logistic1-4 at youtube.

  2. Week 43: Slides. Note (corrected 04.11.21) on Laplace approximation. Practical 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, 5215b 6.

  3. Week 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.

  4. 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.

  5. 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.

  6. Week 47: selfstudy on Markov random fields and Bayesian image analysis.

    We meet 8.15-10 Friday November 26 in 5.227.

  7. 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.


  1. Computation of the likelihood function for GLMMs: deterministic quadrature
  2. Computation of the likelihood function for GLMMs: Monte Carlo methods
  3. Markov random fields: the Hammersley-Clifford theorem
  4. Markov random fields: Brooks factorization and Gaussian Markov random fields
The curriculum for the course and exam consists of the lecture slides including the exercises given in the slides.
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