Curriculum
The curriculum is defined by the Compendium, the Lectures, the Projects and previous Exams.
The Compendium: Bayesian Spatial Inversion - H.Omre, T.M.Fjeldstad and O.B.Forberg - will be distributed in lectures.
The Lectures: Some additional material will be available on this web-site.
The Projects: See Project page on this web-site.
Previous Exams: See Exam page on this web-site.
Preliminary Curriculum in Compendium:
1.Introduction
2.Bayesian Spatial Inversion
3.Conjugate Inversion Models - NOT 3.1 / 3.2
4. Random Fields
Part I.Traditional Conjugate Spatial Models - ALL
Appendices:
A.Classes of Simulation Algorithms
B.Geostatistics: Kriging Prediction Models
D.Clustered/Repulsive Event RF
E.Markov Random Profile - Markov Random Chain - Equivalence
Unit - Continuous Spatial Variables: 4/.1 + 5/.1 + 6/.1 .2 + 7/.1 + 8/.1 + A + B
Unit - Event Spatial Variables: 4/.2 + 5/.2 + 6/.3 .4 + 7/.2 + 8/.2 + D
Unit - Mosaic Spatial Variables: 4/.3 + 5/.3 + 6/.5 .6 + 7/.3 + 8/.3 + E
Alternative Reading:
For continuous spatial variables:
Noel Cressie and Christopher K Wikle. Statistics for Spatio-Temporal Data. (available as e-book trough the library, use oria.no)
- Chapter 1
- Chapter 2
- Chapter 4, 4.1, 4.2, 4.3
For Event spatial variables:
Statistical Analysis and Modelling of Spatial Point Patterns (2008) by Janine Illian, Antti Penttinen, Helga Stoyan, Dietrich Stoyan:
- Chapter 1
- Chapter 4: 4.2, 4.3
For Mosaic spatial variables:
Gaussian Markov Random Fields. Theory and Applications by Håvard Rue and Loenhard Held.
- Chapter 2.2
For Project work:
Bivand, Pebesma & Gomez-Rubio. Applied Spatial Data Analysis with R be useful (available as e-book through the library).
Case Studies:
Gaussian RF: Buland and Omre; 2003: Bayesian linearized AVO Inversion; Geophysics, Vol.68, No.1 pdf