Curriculum

The curriculum is defined by the Compendium, the Lectures, the Projects and previous Exams.

The Compendium: Bayesian Spatial Inversion - H.Omre and +++++ - 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

I.Traditional Conjugate Spatial Models - ALL

Appendices:

A.Classes of Simulation Algorithms

B.Geostatistics: Kriging Prediction Models

D.Custered/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).

2018-02-16, Karl Henning Omre