Syllabus
1. ergodicity and mixing for spatial point processes, limit theorems for point processes and particle processes in large domains, approximation by m-dependent random fields
2. asymptotics for Poisson processes, stabilization theory, chaos decomposition and Stein’s method
3. random fields with continuous parameter, more advanced models, statistical methods, simulation
4. statistical inference for inhomogeneous and space-time point processes
Annotation
The course deals with selected advanced topis in spatial modeling that follow the courses on spatial modeling and spatial statistics from master study. The main topics include limit theorems for point processes and geometric models, statistical inference for random fields, non-stationary models and space-time point processes.
For PhD students.