The area-interaction process and the continuum random-cluster model are characterized in terms of certain functional forms of their respective conditional intensities. In certain cases, these two point process models can be derived from a bivariate point process model which in many respects is simpler to analyse and simulate. Using this correspondence we devise a two-component Gibbs sampler, which can be used for fast and exact simulation by extending the recent ideas of Propp and Wilson. We further introduce a Swendsen-Wang type algorithm. The relevance of the results within spatial statistics as well as statistical physics is discussed
Perfect simulations are methods, which convert suitable Markov chain Monte Carlo (MCMC) algorithms i...
This thesis is about probabilistic simulation techniques. Specifically we consider the exact or perf...
We consider the combination of path sampling and perfect simulation in the context of both likelihoo...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
Markov point processes provide flexible models to describe interaction behavior amongst points, incl...
Markov point processes provide flexible models to describe interaction behavior amongst points, incl...
Markov point processes provide flexible models to describe interaction behavior amongst points, incl...
: Because so many random processes arising in stochastic geometry are quite intractable to analysis,...
We summarize and discuss the current state of spatial point process theory and directions for future...
This work presents a review of some of the schemes used to perfect sample from spatial processes
In this work, we first present a flexible hierarchical Bayesian model and develop a comprehensive Ba...
Perfect simulations are methods, which convert suitable Markov chain Monte Carlo (MCMC) algorithms i...
This thesis is about probabilistic simulation techniques. Specifically we consider the exact or perf...
We consider the combination of path sampling and perfect simulation in the context of both likelihoo...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
Markov point processes provide flexible models to describe interaction behavior amongst points, incl...
Markov point processes provide flexible models to describe interaction behavior amongst points, incl...
Markov point processes provide flexible models to describe interaction behavior amongst points, incl...
: Because so many random processes arising in stochastic geometry are quite intractable to analysis,...
We summarize and discuss the current state of spatial point process theory and directions for future...
This work presents a review of some of the schemes used to perfect sample from spatial processes
In this work, we first present a flexible hierarchical Bayesian model and develop a comprehensive Ba...
Perfect simulations are methods, which convert suitable Markov chain Monte Carlo (MCMC) algorithms i...
This thesis is about probabilistic simulation techniques. Specifically we consider the exact or perf...
We consider the combination of path sampling and perfect simulation in the context of both likelihoo...