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Spatial Modelling, Spatial Statistics 1 - NSTP005

Title:	Prostorové modelování, prostorová statistika 1
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2013 to 2017
Semester:	winter
E-Credits:	6
Hours per week, examination:	winter s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	doc. RNDr. Zbyněk Pawlas, Ph.D.
Class:	DS, pravděpodobnost a matematická statistika
Classification:	Mathematics > Probability and Statistics
Pre-requisite :	NSTP050
Interchangeability :	NMTP438
Is co-requisite for:	NSTP154
Is incompatible with:	NMTP438
Is interchangeable with:	NMTP438

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: T_KPMS (19.05.2008)

The lecture involves three parts of spatial modeling and statistics. The first part id devoted to point processes, in particular to finite point processes with a density with respect to the Poisson process. In the second part stationary random processes defined on a continuous domain are studied including the modelling of spatial dependence and spatial prediction. In the last part lattice models are considered, especially Gaussian and Markov random fields.

Aim of the course -

Last update: doc. RNDr. Zbyněk Pawlas, Ph.D. (05.09.2012)

Introduce students into the basic methods for modelling and statistical analysis of spatial data.

Literature -

Last update: doc. RNDr. Zbyněk Pawlas, Ph.D. (05.09.2012)

Cressie N.: Statistics for spatial data. Wiley, 1993.

Schabenberger O., Gotway C.: Statistical models for spatial data analysis. Chapman&Hall/CRC, 2005.

Teaching methods -

Last update: G_M (27.05.2008)

Lecture+exercises.

Syllabus -

Last update: doc. RNDr. Zbyněk Pawlas, Ph.D. (05.09.2012)

1. Spatial point processes, Poisson process, interaction models, characteristics, summary statistics.

2. Geostatistics, random fields, variogram, spatial prediction.

3. Markov random fields, Gaussian models, lattice models.