spatial point process models
Spatial point process models for MRI lesion data in multiple sclerosis
... Statistical methodology for point pattern data advanced rapidly in the second half of the 20 th century. One-dimensional non-homogeneous Poisson processes have been used to model, for example, geomagnetic reversal ...
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Point process based models for rainfall.
... Poisson process (in time) of rate A and generate a number, C, of rain ceUs, th a t occur in a Neyman-Scott ...the spatial structure of rainfaU are similar to the ones described in Section ...
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Joint Models for Spatial and Spatio-Temporal Point Processes
... a point mass at zero and a Poisson component with component membership for observations being ...Cox process for these types of ...Cox process to model an unobserved point process ...
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Stochastic simulation and spatial statistics of large datasets using parallel computing
... the spatial distribution of trees in East- Central European forests using the K-function with edge correction done using the border method in a circular spatial ...small spatial scale which give a ...
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An Analysis Framework for Inter-User Interference in IEEE 802.15.6 Body Sensor Networks: A Stochastic Geometry Approach
... Matern point process models the spatial distribution of nodes using ...Poisson point process such that the distance between any two nodes in the Matern thinning is larger than a ...
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Computationally Efficient Estimation of Non-stationary Gaussian Process Models for Large Spatial Data.
... iterative process, also called Voronoi iterations, is closely related to the k-means ...random point pattern is iteratively ...the point pattern used for the following ...
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An autoregressive point source model for spatial process
... for Models 3, 5, and ...for Models 3 and ...both Models 3 and 5, fitted variances are a function of number of neighbors and they increase away from the ...stronger spatial patterns and ...
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Quantification of annual wildfire risk; A spatio-temporal point process approach.
... a method for Bayesian inference in structured additive regression models with a latent Gaussian field, like our model. INLA is an alternative to MCMC and combines analyti- cal approximations with numerical ...
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Stochastic Geometry Analysis of Ultra Dense Network and TRSC Green Communication Strategy
... chapter models for double-layer heterogeneous relays in UDN based on spatial homogeneous Poisson point process, and derives the SINR distribution and the average achievable rate of the ...
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Point process models for spatio temporal distance sampling data from a large scale survey of blue whales
... 2. The blue whale survey data. Line-transect cetacean surveys were carried out in the Eastern Tropical Pacific Ocean (ETP) between 1986 and 2007. Figure 1 shows the survey region and transect lines over this whole ...
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Analyse problems, not data:One world, one health
... of spatial statistical methods in the ...parametric models for spatial lattice data and spatial point process data, respectively, but were forced to use ad hoc methods for ...
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Time-clustering investigation of fire temporal fluctuations in Portugal
... the spatial and temporal vari- ability of forest fire activity and the synoptic patterns asso- ciated with air masses favourable to high wildfire activity in Portugal and conclude that, i) about 80% of total A is ...
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Bayesian P splines and advanced computing in R for a changepoint analysis on spatio temporal point processes
... tial inhomogeneity, spatial dependence and temporal dependence. Many promis- ing extensions are possible. It is worth mentioning the role of the prior distribu- tions: they can concern the number of changepoints, ...
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Markov chain Monte Carlo methods for state space models with point process observations
... For spike train classification, Salimpour, Soltanian-Zadeh, Salehi, Emadi, and Abouzari (2011) show an interesting approach of using the likelihood based on filtered estimates as a discriminator for spike trains ...
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International Journal of Computer Science and Mobile Computing
... Spatial filter can be classified into i) smoothing spatial filters and ii) sharpening spatial filters. These filters can be either linear or nonlinear. In linear filter each pixel value in the output ...
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Reasoning, models, and images: behavioral measures and cortical activity
... rotating models, not images. Models that may be spatial in form ( Johnson-Laird, 1998; Kosslyn, 1994, ...the process of reasoning and can impede the ...mental models from all sorts of ...
15
Simulation and application of spatial autoregressive geographically weighted regression model (SAR GWR)
... The parameters estimation for SAR-GWR is motivated by combining the parameters estimation for SAR and GWR. The efficient estimator for SAR model is an Instrumental Variable approach which can calculate using two-stage ...
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Modelling the statistical dependence of rainfall event variables through copula functions
... One of the most helpful advantages of the copula approach is actually the availability of a number of parametric copula families, whose members can be defined in any multivari- ate case. The majority of models ...
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Approximate Bayesian computing for spatial extremes
... a spatial process model so that the spatial dependence may be ...of models are max-stable ...max-stable process models have been described (Schlather, 2002; Kabluchko ...these ...
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Drift term and vertex point in single factor interest rate model
... Ornstein-Uhlenbeck process, the drift term that is obtained plays a crucial role in determining the vertex point of monetary ...rate models of Vasicek and Cox Ingersoll ...rate models are ...
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