No. 938

Title:

Bayesian Estimation of Soft-Core Interaction Potential Models for Spatial Point Patterns

Author(s):

OKABE Masahilo (Department of Statistical Science, The Graduate University for Advanced Studies) ;
TANEMURA Masaharu (The Institute of Statistical Mathematics)

Key words:

Repulsive interaction; Soft-Core models; Bayesian estimation; MCMC methods; L-statistics.

Abstract:

   For a spatial pattern of points interacting with a repulsive potential in a given finite region of the plane, Bayesian estimation of parametric interaction potential functions between individuals (the Soft-Core models) is proposed. The computations are performed by the use of MCMC (Markov Chain Monte Carlo) methods. We consider two prior distributions with the jumping distributions within Markov chain simulations. Simulated marginal posterior density distributions of model parameters are fitted to the generalized gamma distribution. We compare marginal posterior modes with the maximum likelihood estimates of the model parameters. The validity of our procedure is graphically demonstrated by the L-statistics. As illustrations, several examples of its application to real data are presented.