MA8702 --- 2018

Tenative syllabus (will be completed along the way)

Week 3: Parameter estimation and prediction in Gaussian processes, Gaussian random fields and Gaussian Markov random fields. GP note, Spatial regression, GMRF, link to SPDE paper
Week 4: Project on GPs!
Week 5: Latent Gaussian models and integrated nested Laplace approximations (INLA). Paper, Overview tutorial
Week 6: Project on INLA!
Week 7(12 feb): Template model builder and automatic differentiation. Prepare as follows: TMB start guide
Week 7(13 feb): Template model builder. Link to paper: TMB paper
Week 8: Ensemble Kalman filter & related filters. Link to paper: http://www.jonathanrstroud.com/papers/enkf-tutorial.pdf
Week 9: Properties of the ensemble Kalman filter. Link to paper: http://onlinelibrary.wiley.com/doi/10.1111/sjos.12039/full
Week 10: Exact recursions for Hidden Markov models
Week 11: NO LECTURE 13 MARCH.
Week 12: Project on Hidden Markov models!
Week 13: NO LECTURES IN EASTER BREAK.
Week 14: Recursive calculations on graphs.
Week 15: Recursive optimization. Link to papers: https://link.springer.com/chapter/10.1007/978-3-540-74048-3_4 (DTW, Chapter 4.1-2 in Muller), https://www.sciencedirect.com/science/article/pii/S0377221713003834 (Martinelli et al.)
Week 16: Dimension reduction. Link to papers:https://www.tandfonline.com/doi/abs/10.1198/106186008X318440 (MDS), https://onlinelibrary.wiley.com/doi/abs/10.1002/wics.51 (PLS)