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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 2018
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:	cancelled
Language:	Czech
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

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Annotation -

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.

Last update: T_KPMS (19.05.2008)

Aim of the course -

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

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

Literature -

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

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

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

Teaching methods -

Lecture+exercises.

Last update: G_M (27.05.2008)

Syllabus -

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.

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