Search results for "Gibbs proce"
showing 5 items of 5 documents
Multiscale processes to describe the Eastern Sicily Seismic Sequences
2018
In this paper, a version of hybrid of Gibbs point process models is proposed as method to characterise the multiscale interaction structure of several seismic sequences occurred in the Eastern Sicily in the last decade. Seismic sequences were identified by a clustering technique based on space-time distance criterion and hierarchical clustering. We focus our analysis on five small seismic sequences, showing that two of these are described by an inhomogeneous Poisson process (not significant interaction among events) while the other three clusters are described by a hybrid-Geyer process (mutiscale interaction between events). The proposed method, although it still needs extensive testing on …
Bayesian Smoothing in the Estimation of the Pair Potential Function of Gibbs Point Processes
1999
A flexible Bayesian method is suggested for the pair potential estimation with a high-dimensional parameter space. The method is based on a Bayesian smoothing technique, commonly applied in statistical image analysis. For the calculation of the posterior mode estimator a new Monte Carlo algorithm is developed. The method is illustrated through examples with both real and simulated data, and its extension into truly nonparametric pair potential estimation is discussed.
Recent applications of point process methods in forestry statistics
2000
Forestry statistics is an important field of applied statistics with a long tradition. Many forestry problems can be solved by means of point processes or marked point processes. There, the "points" are tree locations and the "marks" are tree characteristics such as diameter at breast height or degree of damage by environmental factors. Point pro- cess characteristics are valuable tools for exploratory data analysis in forestry, for describing the variability of forest stands and for under- standing and quantifying ecological relationships. Models of point pro- cesses are also an important basis of modern single-tree modeling, that gives simulation tools for the investigation of forest stru…
Local LGCP estimation for spatial seismic processes
2020
Using recent results for local composite likelihood for spatial point processes, we show the performance of advanced and flexible statistical models to describe the spatial displacement of earthquake data. Local models described by Baddeley (2017) allow for the possibility of describing both seismic catalogs and sequences. When analysing seismic sequences, the analysis of the small scale variation is the main issue. The interaction among points is taken into account by Log-Gaussian Cox Processes models through the estimation of the parameters of the covariance of the Gaussian Random Field. In their local version these parameters are allowed to vary spatially, and this is a crucial aspect fo…
Advanced spatio-temporal point processes for the Sicily seismicity analysis
2018
Due to the complexity of the generator process of seismic events, we study under several aspects the interaction structure between earthquake events using recently developed spatio-temporal statistical techniques and models. Using these advanced statistical tools, we aim to characterise the global and local scale cluster behaviour of the Easter Sicily seismicity considering the catalogue data since 2006, when the Italian National Seismic Network was upgraded and earthquake location was sensibly improved. Firstly, we characterise the global complex spatiotemporal interaction structure with the space-time ETAS model where background seismicity is estimated non-parametrically, while triggered …