Syllabus
Geof H. Givens, Jennifer A. Hoeting: Computational Statistics, 2nd Edition
ISBN: 978-0-470-53331-4.
In the link to the book web-page you also find datasets and R-scripts connected to examples/exercises in the book
- Chapter 1 - Background: Will only be referred to when needed
- Chapter 2 - Optimization: General methods. Everything except
- the details about convergence rates (all methods)
- Chapter 3 - Combinatorial optimization. Everything except
- section 3.4.2
- Chapter 4 - The EM algorithm
- Section 4.1: All
- Section 4.2: All but except 4.2.3.2
- Chapter 6 - Monte Carlo methods
- Not section 6.3.1.1
- 20.05.19, page 19: Derivation corrected and star taken away from hat mu.
- 20.05.19: Page 21: Corrected hat mu_IS^* to hat mu_IS
- 20.05.19: Part 2, page 3, 1/n removed from second estimate.
- Chapter 7 - Markov Chain Monte Carlo
- 20.05.19: Page 24: More clearly specified which index that grows when looking at limits.
- 20.05.19: Error on eps/2 term corrected on page 40
ADMM algorithm as presented in these slides.
Storvik (2019): Sequential Monte Carlo for state space models.
Storvik (2020): The Stochastic gradient algorithm
- In section 4, only Lemma 1 and Theorem 1 (as well as their proofs) is part of the syllabus
Hamiltonian Monte Carlo, & Variational inference, as presented in these three slide packs:
All given exercises and published code.
STK 9051 also:
Section1-3 of (HMC-Article) MCMC using Hamiltonian dynamics Radford M. Neal, University of Toronto (2011), arXiv:1206.1901
Section 1-3 in VI-Article David M. Blei, Alp Kucukelbir & Jon D. McAuliffe (2017) Variational Inference: A Review for Statisticians, Journal of the American Statistical Association, 112:518, 859-877, DOI: 10.1080/01621459.2017.1285773
There are some misprints in the book. Some are given at through the book webpage. Some additional misprints found by the lecturer is given here. If you find more errors, please let me know.