in this paper, the effect of size on melting temperature of metallic nanoparticles (au, pb and bi) is theoretically simulated and explained. in this regard, the cause of difference in various experimental data is introduced, which is the difference between nanoparticles’ grain gaussian distribution. this volume-depended model with the help of the gaussian distribution can describe the relation ...

In this paper, circular Lorentz–Bessel–Gaussian beams (CLBGBs) are introduced as a novel member of the Lorentz-Gaussian family. The analytical expression propagation these passing through paraxial optical ABCD system is derived. generalized Huygens-Fresnel diffraction integral form Collins’s formula and expansion Lorentz distribution in terms complete orthonormal basis set Hermite-Gauss modes u...

The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP...

The self-organizing map (SOM) algorithm for finite data is derived as an approximate MAP estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior, which is equivalent to a generalized deformable model (GDM). For this model, objective criteria for selecting hyperparameters are obtained on the basis of empirical Bayesian estimation and crossvalidation, which are represent...

In this paper we extend our previous work, first presented in, to handle effectively non-Gaussian processes and long-time integration in unsteady simulations of compressible flows. Specifically, we apply the generalized polynomial chaos (GPC) method to solve the one-dimensional stochastic Euler equations. We present systematic verification studies against an analytical solution of the stochasti...

Non-Line-of-Sight (NLoS) propagation condition is a crucial factor affecting the precision of localization in Ultra-Wideband (UWB) Indoor Positioning System (IPS). Numerous supervised Machine Learning (ML) approaches have been applied for NLoS identification to improve accuracy IPS. However, it difficult existing ML maintain high classification when database contains small number signals and la...

In this paper we propose a feasible way to price American options in a model with time varying volatility and conditional skewness and leptokurtosis using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk neutral dynamics can be obtained in this model using the Generalized Local Risk Neutral Valuation Relationship of Duan (1999) and we derive approximation proce...

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoot...

In this thesis, I propose and implement a generalized Gaussian methodology to accomodate the asymmetry and kurtosis of financial returns data, features not captured by standard Gaussian methods. The methodology extends from Gaussian methods with one variance-covariance matrix by estimating different variance-covariance matrices to characterize the differential risk exposures in long and short p...