Subjects

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Spatial Modelling, Spatial Statistics 2 - NSTP154

Title:	Prostorové modelování, prostorová statistika 2
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2018
Semester:	summer
E-Credits:	6
Hours per week, examination:	summer 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
Co-requisite :	NSTP005
Interchangeability :	NMST543

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

The course is continuation of NSTP005. Theory of point processes is extended in two directions, both marked point processes and inhomogeneous point processes are considered. More attention is devoted to advanced statistical methods. The final part of the course is dealing with geostatistics, it contains hierarchical models of spatial data and the use of Bayesian approach.

Last update: T_KPMS (13.05.2010)

Aim of the course -

The subject enables students to learn some advanced topics from spatial stochastic processes and their statistical analysis.

Last update: T_KPMS (13.05.2010)

Literature - Czech

Banerjee S., Carlin B. P., Gelfand A.: Hierarchical Modeling and Analysis for Spatial Data. Chapman&Hall/CRC, 2004.

Illian J., Penttinen A., Stoyan H., Stoyan D.: Statistical Analysis and Modelling of Spatial Point Patterns. Wiley, 2008.

Moller J., Waagepetersen R. P.: Statistical Inference and Simulation for Spatial Point Processes. Chapman&Hall/CRC, 2003.

Last update: T_KPMS (13.05.2010)

Teaching methods -

Lecture+exercises.

Last update: G_M (27.05.2008)

Syllabus -

1. Marked point processes, models, estimation of characteristics, tests of independence.

2. Inhomogeneous point processes, model fitting and diagnostics.

3. Hierarchical spatial models, Bayesian statistical analysis.

Last update: T_KPMS (13.05.2010)