Asymptotic inference for stochastic geometry models

Abstract

The thesis studies three methods used in stochastic geometry in order to investigate asymptotic behaviour of random geometrical structures in large domains or in a large intensity regime. In particular, it deals with the Malliavin-Stein method, the method of stabilization and the method of cumulants.

Keywords

• Malliavin calculus
• Stein’s method
• stabilization method
• cumulant method
• point processes
• random tessellations
• random graphs
• particle processes
• Poisson point processes
• Brillinger-mixing point processes
• Gibbs point processes