Topics will be clarified in first lecture, partly based on PhD topics of participants. The following topics will be included as part of curriculum:

• Gaussian processes and Gaussian Markov random fields. Spatial regression, BayesOpt_EI, GMRF, link to SPDE paper
• Latent Gaussian models and inference in such classes: Integrated Laplace Approximations, Template model builder Paper, INLA more tutorial, Overview blog INLA, TMB start guide, TMB paper
• Markov chain Monte Carlo methods (Hamiltonian Monte Carlo, adaptive sampling) link to paper with 50 y of MH algorithm, Hamiltonian MC, Betancourt, Hamiltonian MC, Neal
• Sequential Monte Carlo methods Tutorial PF, Tutorial ENKF
• High-dimensional distancesClustering / dim reduction techniques. 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.tandfonline.com/doi/abs/10.1198/106186008X318440 (MDS). https://doi.org/10.1348/000711005X48266 (k-means clustering).