# Spatial Statistics with Image Analysis, HT16

## News

Introductory lecture F0 given at the Image analysis (FMA170) course on 2016-10-06.

## Examination

Three home assignments/projects. The assignments will be handed out during the 2:nd, 4:th and 6:th course week.

v2: 1) Classic Kriging
Handed out 2016-11-07, due 2016-11-22 23:59.

v4: 2) Gaussian Markov fields and non-Gaussian observations
Handed out 2016-11-21, due 2016-12-06 23:59.

v6: 3) MRFs, classification, and space-time data
Handed out 2015-12-05, due 17:00 the day before your presentation.
Written and oral presentation of project 3.

## Literature

Most of the material is coverd in:

Both available as E-books from the Lund University Libraries.

## Schedule

Recommended reading in parentheses (HS:? - Chapters in the Handbook of Spatial Statistics; SST:? - Spatial and Spatio-temporal Bayesian Models with R-INLA).

DayLecture/ExerciseHandout
w1/44Mon31/10L1 Introduction:
Statistical modelling
(1)
F01.pdf
C0 Image data and dependence in Matlab (Work at home) lab0.pdf
Fri4/11L2 Spatial models I:
Random fields and covariance models
(HS: 2.1, 2.2, 2.5-2.7)
Spectral representation
(HS: 5.1-5.2)
F02.pdf
w2/45Mon7/11L3 Spatial models II:
Classic Kriging and covariance estimation (LS)
(HS: 2.3, 2.8, 3)
F03.pdf
Thu10/11C1 Covariance estimation lab1.pdf
Fri11/11L4 Spatial models III:
Discussion of Lab 1 and covariance estimation (ML and REML estimation)
(HS: 4.1-4.3)
F04.pdf
w3/46Mon14/11L5 Markov fields I:
Gaussian fields
(HS: 12.1.1-12.1.4, 12.1.7)
F05.pdf
Thu17/11
18/11
C2 Work on HA1
Fri18/11L6 Markov fields II:
Gaussian fields
(SST: 6-6.1; HS: 13.2)
F06.pdf
w4/47Mon21/11L7 Hierarchical models II:
Bayesian analysis
(HS: 7.1-7.3; SST: 3-3.5)
Non-Gaussian data
(HS: 7.4-7.5; SST: 5-5.3, 4.6-4.7)
Approximate inference for latent GMRFs (pdf)
F07.pdf
Thu24/11C3 Gaussian Markov random fields lab3.pdf
Fri25/11L8 Markov fields III:
Spectral representation and Matern-like precision models
(SST: 6.4-6.5)
GMRFs and SPDEs (pdf)
F08.pdf
w5/48Mon28/11L9 Classification F09.pdf
Thu1/12C4 Work on HA2
Fri2/12L10 Gibbs-sampling and parameter estimation
(SST: 4-4.5.1)
F10.pdf
w6/49Mon5/12L11 Markov fields III:
Discrete fields
(HSS: 12.1.8)
F11.pdf
Thu8/12C5 Discrete field simulation and estimation lab5.pdf
Fri9/12L12 Markov fields IV:
Parameter estimation for discrete fields
(HSS: 12.1.9)
F12.pdf
w7/50Mon12/12L13 Thesis projects and Comments regarding Projects 1 and 2 F13.pdf
Wed14/12C6 Work on HA3

## Prerequisits

A basic course in mathematical statistics, and at least one of:

## Recommended related courses (not prerequisits)

Questions: Validate: HTML CSS

## Course Information

TBA
 LTH Code: FMSN20 NF Code: MASM25 Credits: 7.5 Level: Advanced Level Language: English First lecture: October 31 2016 at 15:15 in MH:362D Lectures: Monday15-17, MH:362D Friday v2: 10-12, MH:333 v3-7: 10-12, MH:228 Computer exercises: v2-v6: Thursday 9-12 v2-v6: Wednesday 13-16 v7: Wednesday 9-12 E:Saturnus Office hours: Lecturer: Johan Lindström046-222 40 60MH:319 Teaching assistants: Unn Dahlen, MH:327b

## CEQ

CEQ - Spatial Statistics