We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance- covariance matrix. Specifically, we introduce a new distribution called the truncated G-Wishart distribution that has support over precision matrices that lead to positive associations between the random effects of neighboring regions while preser...

This paper details a methodology for taking into account seismic information into 3D high resolution stochastic simulations of lithofacies. The simulation is based on a non stationary truncated gaussian approach. Within this frame, a 3D matrix of facies proportion is built. It is estimated from both well and seismic-derived proportions. Thus the seismic attributes are previously turned into sof...

Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling abilities, due to the Gaussian assumption. In this paper, we introduce a novel variant of GGMs, which relaxes the Gaussian restriction and yet admits efficien...

Continuous density functions and their truncated versions are widely used in engineering practice. However, limited work was dedicated to the theoretical analysis and presentation in closed forms of truncated density functions of discrete random variables. The derivations of exponential, uniform and Gaussian discrete and truncated density functions and related moments, as well as their applicat...

One method to characterize analogue to digital converters (ADCs) is to use a histogram, where Gaussian noise may be used as stimulus signal. However, a Gaussian noise signal that excites all transition levels also generates input values outside working range of the ADC. Modern signal generators can generate arbitrary signals. Hence, excluding undesired values outside the ADC full scale can mini...

In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure ...

We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The truncated variables are assumed latent and integrated out to induce a marginal model. We show that the variables in the marginal model are non-Gaussian distr...

in this paper, the detector of moving target in mimo radar with widely separated antenna in the presence of α-stable clutter is proposed. due to the complexity of unknown parameter estimation in the presence of target, the rao test is applied. the α-stable model is a general model that can applied to different environments with appropriate selection of parameters. the problem with this model is...

In this letter, we investigate the mutual information rate (MIR) achieved by an independent identically distributed (IID) Gaussian input on intensity-driven signal transduction channel. Specifically, asymptotic expression of continuous-time MIR is given. Next, aiming at low computational complexity, also deduce approximately numerical solution for MIR. Moreover, corresponding lower and upper bo...