gaussian random field model
A Novel Hybrid Image Denoising Technique based on Trilateral Filtering and Gaussian Condition Random Field Model
... a Gaussian Conditional Random Field (GCRF) model which was ...different model for each individual noise level, the supposed deep network modeled the input noise ...So model was ...
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Kriging and Simulation in Gaussian Random Fields Applied to Soil Property Interpolation
... The simulation employed in this paper is conditional simulation. The simulation is conditional on parameters of variogram and covariance models. An important part of spatial modeling is related to the speciation of the ...
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Uncertainty quantification for flow and transport in highly heterogeneous porous media based on simultaneous stochastic model dimensionality reduction
... generating Gaussian random fields Z, for instance, the circular embedding algorithm (see, ...a Gaussian random field, the numerical implementation is not trivial and, therefore, it is ...
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Variational study of the random-field XY model
... disorder-dependent Gaussian variational approach is applied to the d -dimensional ferromagnetic XY model in a random ...a random mass term in correlation ...
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Gaussian Markov Random Field Models for Surveillance Error and Geographic Boundaries
... to model heterogeneous surveillance error and using the inspector-to-household assignment information can be of great use in reducing the error in infestation ...this model can estimate varying levels of ...
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INTERACTING THROUGH DISCLOSING: PEER INTERACTION PATTERNS BASED ON SELF DISCLOSURE LEVELS VIA FACEBOOK
... flow field. Second, the observed flow field in each flow-block is treated as 2D distribution of samples and mixtures of Gaussian is used to parameterize it by keeping the generality of flow ...
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Associations Between Gaussian Markov Random Fields and Gaussian Geostatistical Models with an Application to Model the Impact of Air Pollution on Human Health
... Poisson model is a natural choice for modeling count data, it requires the strict assumption that the mean and variance of the response variable are ...we model mortality counts using a generalized Poisson ...
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Near Lossless Compression Based on a Full Range Gaussian Markov Random Field Model for 2D Monochrome Images
... Range Gaussian Markov Random Field (FRGMRF) model for monochrome image compres- sion, where images are assumed to be Gaussian Markov Random ...the model are estimated ...
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A comparative study of Gaussian geostatistical and Gaussian Markov random field models
... smoothness parameter 0.5 and nugget 0 for the GGP and a binary scaled weight neighbor- hood structure for the GMRF. The simulation is composed of two steps, the estimation of parameters and the prediction of data. In the ...
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Estimation of Graphical Models through Structured Norm Minimization
... In this paper, a new structured norm minimization method for solving multi-structure graphical model selection problems is proposed. Using the proposed SSON, we can efficiently and accurately recover the ...
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Accelerated degradation test for simple step-stress model using inverse gaussian process
... inverse Gaussian, it is logical to use it as a life time ...of random vibrations, increases temperature, voltage or ...Inverse Gaussian process is useful as a repair time ...the field of ...
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Correct Classification Rates in Multi Category Discriminant Analysis of Spatial Gaussian Data
... Šaltytė and Dučinskas [11] derived the asymptotic approximation of the expected error rate when classifying the observation of a scalar Gaussian random field into one of two classes with different ...
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Application of truncated gaussian simulation to ore-waste boundary modeling of Golgohar iron deposit
... The ore and the waste domains within the deposit can be simulated in a block grid with 10*10*10 meters by use of the obtained parameters from truncated gaussian model, including flag, truncation threshold ...
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Quantifying the changes of soil surface microroughness due to rainfall impact on a smooth surface
... a field plot via a rainfall ...the random roughness (RR) index, the crossover length, the variance scale from the Markov– Gaussian model, and the limiting ...
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Trend Estimation
... a random or deterministic function of ...a random function (e.g. a random walk, or a Gaussian process), although this distinction becomes more blurred in the case of ...
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Local properties and statistics of phase singularities in generic wavefields
... in random scalar waves which are isotropic (for propagation in both two and three ...the model of gaussian random waves is constructed (section 3), and applied to four particular spectra - ...
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Simulating Solute Transport in Porous Media Using Model Reduction Techniques
... cess model as “data” that provides the basis for a reduced ...process model, in this case a contaminant transport simulation, forward in time, recording “snap- shots” of contaminant ...the model ...
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PLANT GROWTH MODELING OF ZINNIA ELEGANS JACQ USING FUZZY MAMDANI AND L SYSTEM APPROACH WITH MATHEMATICA
... We can learn the probability model of the mixture topic distribution for each scene categories by the foregoing approach. SVM is widely used in computer vision [14][15] and applied to category the scenes in this ...
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Reflection Scattering Matrix of the Ising Model in a Random Boundary Magnetic Field
... The action (2.1) describes n copies of the Ising model hinged together at the boundary (Fig. 2). In addition to the coupling to the (mean) boundary magnetic field, the new interaction in the action (2.1) ...
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Level and width statistics of the open many-body systems
... Figure 6. The NNLS distribution (a) and the width distribution(b) obtained from the TBRE+CC model in the energy interval 5.0 ≤ E ≤ 25.0 for κ = 0.1(dotted line), κ = 1.0 (solid line), κ = 10.0 (dot-dashed line) ...
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