Normal Distribution Monte Carlo Method
Mostrando 1-12 de 12 artigos, teses e dissertações.
-
1. A Novel In Silico Monte Carlo Approach to Optimize a PSD Estimation Problem. Generation of Data Fusion Experiment Rules
ABSTRACT This article analyzes the performance of combining information from Scanning Electron Microscopy (SEM) micrographs with Static Light Scattering (SLS) measurements for retrieving the so-called Particle Size Distribution (PSD) in terms of experimental features. The corresponding data fusion is implemented using a novel Monte Carlo-based method consist
Trends in Computational and Applied Mathematics. Publicado em: 2022
-
2. Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significan
Braz J Med Biol Res. Publicado em: 13/11/2017
-
3. PRESSUPOSTO DA NORMALIDADE MULTIVARIADA PARA O TESTE DE RAZÃO DE VEROSSIMILHANÇA ENTRE DOIS GRUPOS DE CARACTERES DE MAMONEIRA / ASSUMPTION OF MULTIVARIATE NORMALITY FOR THE LIKELIHOOD RATIO TEST BETWEEN TWO GROUPS OF CHARACTERS OF CASTOR BEANS
The likelihood ratio test for independence between two groups of variables allows us to identify whether there is a dependency relationship between two groups of variables, ie, if the covariance between the two groups are zero. This test assumes normality multivariate data, which limits its application, in many studies of agronomic area, times when you need
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 29/02/2012
-
4. Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series
In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space
Pesqui. Oper.. Publicado em: 05/07/2012
-
5. Skew normal mixed models in microarray data generated from complex pedigrees / Modelos mistos normais assimÃtricos em dados de microarrays originados de pedigrees complexos / Modelos mistos normais assimÃtricos em dados de microarrays originados de pedigrees complexos / Skew normal mixed models in microarray data generated from complex pedigrees
Estimates of heritability for gene expression are scarce and commonly originated from family structures, in which the variability of responses among and within families are provided under a uniform covariance structure for related individuals, ignoring the known relationship among all individuals in the pedigree. Gauss-Markov normal mixed models are the usua
Publicado em: 2009
-
6. Improvement of Wald residual in generalized linear models / Melhoramento do resíduo de Wald em modelos lineares generalizados
The theory of generalized linear models is very used in statistics, not only for modeling data normally distributed, but in the modeling of data whose distribution belongs to the exponential family of distributions. Some examples are binomial, gamma and inverse Gaussian distribution, among others. After tting a model in order to check the adequacy of tting,
Publicado em: 2008
-
7. Análise bayesiana do modelo fatorial dinâmico para um vetor de séries temporais usando distribuições elípticas. / Bayesian Analysis of the dynamic factorial models for a time series vector using elliptical distribuitions.
The factor analysis is an important statistical tool that has wide practical applications and it explains the correlation among a large number of observable variables in terms of a small number of unobservable variables, known as latent variables. The proposal of this work is the Bayesian analysis, which incorporates the information we have concerning the pa
Publicado em: 2008
-
8. Bayesian multiple comparisons in homocedastic and heterocedastic normal models. / ComparaÃÃes MÃltiplas Bayesianas em Modelos Normais HomocedÃsticos e HeterocedÃsticos
Multiple comparison procedures are used to compare factorâs levels means. Nevertheless, the most popular tests show problems concerning the ambiguity of results and the control of type I error. Methods based on cluster analysis have been proposed to avoid ambiguity, but they present the problem of being valid only under normality. An alternative to avoid th
Publicado em: 2008
-
9. Sthocastic model to estimate the soybean productivity in the State of São Paulo through bivaried normal simulation / Modelo estocástico para estimação da produtividade de soja no Estado de São Paulo utilizando simulação normal bivariada
The availability of resources, as much of financial order and human labor, is scarse. Therefore, it must stimulates the regional planning that minimizes the use of resources. Then, the forecast of harvests through modelling techniques must previously on the basis of be carried through the regional characteristics, thus indicating the routes of the research,
Publicado em: 2007
-
10. AnÃlise topolÃgica de redes de ligaÃÃes de hidrogÃnio em um sistema modelo
An analysis of the local and global topological properties of the hydrogen bonds networks between water molecules, generated by Monte Carlo (MC-NPT) simulations near to supercritical conditions and using the TIP5P model potential for water-water interactions, indicates the appearance of small worlds patterns with large clustering coefficient and small path l
Publicado em: 2006
-
11. Proposta e AvaliaÃÃo de CritÃrios de ConvergÃncia para o MÃtodo de Monte Carlo via Cadeias de Markov: Casos Uni e Multivariados. / Proposal and evaluation of convergence diagnostics criterion for Markov Chain Monte Carlo methods: univariate and multivariate cases.
Markov Chain Monte Carlo Methods have been studied in several areas, but one of the largest difficulties is to determine the appropriate sample size, i.e., to determine the convergence of the process and then make inference over parameters of the target distribution. In spite of several accessible convergence diagnostic procedures in literature we have opted
Publicado em: 2004
-
12. Catalytic tempering: A method for sampling rough energy landscapes by Monte Carlo
A new Monte Carlo algorithm is presented for the efficient sampling of the Boltzmann distribution of configurations of systems with rough energy landscapes. The method is based on the introduction of a fictitious coordinate y so that the dimensionality of the system is increased by one. This augmented system has a potential surface and a temperature tha
The National Academy of Sciences.