The "traditional" use of the Positive Deviance approach to behavior change involves studying children who thrive despite adversity, identifying uncommon model behaviors among Positive Deviant families, and then designing and implementing an intervention to replicate these behaviors among mothers of malnourished children. This article presents the results of a literature review designed to gathe...

A stochastic volatility-in-mean model with correlated errors using the generalized hyperbolic skew Student-t (GHST) distribution provides a robust alternative to the parameter estimation for daily stock returns in the absence of normality. An efficient Markov chain Monte Carlo (MCMC) sampling algorithm is developed for parameter estimation. The deviance information, the Bayesian predictive info...

An image registration method for panoramic image generation is described. For the problem, estimation of overlapping regions is essential and the SSD (Summation of Squared Differences) method has been used as a cost function of the optimal problem. But there are some problems in the SSD method. To resolve the problems we propose an image registration method based on the WMDL (Weighted Minimum D...

A Bayesian hierarchical framework with a Gaussian copula and generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This model was applied to summer temperatures over Extremadura Region, southwest Spain, during period 1980–2015, compared noncopula model. The implemented Monte Carlo Markov Chain (MCMC) method that allows model’s par...

In many types of statistical modeling inequality constraints are imposed between the parameters of interest. As we will show in this paper, the DIC (i.e., posterior Deviance Information Criterium as proposed as a Bayesian model selection tool by Spiegelhalter et al., 2002) fails when comparing inequality constrained hypotheses. In this paper we will derive the prior DIC and show that it also fa...

Rsumm La ddcouverte des rgles d'association est l'un des principaux probllmes de l'extraction de connaissances dans les bases de donnnes. De nombreux algorithmes eecaces ont tt proposss, dont les plus remarquables sont Apriori, l'algorithme de Mannila, Partition, Sampling et DIC. Ces derniers sont tous basss sur la mmthode de recherche de Apriori: l''lagage du treillis des parties (treillis des...

We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most (online and real-world) social networks are not fully observable. We establish both proper and improper PAC learnability of influence functions under randomly missing observations. Proper PAC learnability under the Discrete-Time Linear Thres...

As a key in cognitive radio networks (CRNs), dynamic spectrum access needs to be carefully designed to minimize the interference and delay to the primary (licensed) users. One of the main challenges in dynamic spectrum access is to determine when the secondary (unlicensed) users can use the spectrum. In particular, when the secondary user is using the spectrum, if the primary user becomes activ...

A neural networks based direct inverse controller for Pressure Process Rig® system is presented, including with the performance analysis using an open-loop and a closed loop system. In order to enhance the performance characteristics of this direct inverse controller, a Fine-Tuning method is proposed. Experimental results show that the open-loop system shows lower MSE compare with that of the c...

The estimation of the disease incidents was previously analyzed using a classical approach. However, this approach features large outlying relative risks and considered as misleading due to several major problems. Some approaches such as the hierarchical Bayesian method have been adopted in the literature in order to overcome these problems. The purpose of this study is to compare between hiera...