876 resultados para Modeling Rapport Using Hidden Markov Models
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Environmental fragility models are important decision tools for policy makers as they help quantify environmental sensitivity and understand the relationship between human activities and environmental quality. The objective of this study was to evaluate three different environmental fragility models within the Brazilian rainforest region and to use the results to develop environmental zone classes. Two rural river basins located in Ibiuna, Sao Paulo state, Brazil, were studied. Input variables, including slope class, relief dissection rate, soil class, lithology, land cover, and climate data, were used to compute environmental fragility classes using three standard models. The model outputs were evaluated on their ability to accurately predict the most sensitive and least sensitive areas. The best models for each region were used to derive environmental zoning maps, including restoration priorities, best regions for agriculture, and areas with high needs for soil management. These maps will help support land use strategies for environmental restoration. This study provides insight into territorial ordering and management of environmental services with a regional perspective.
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In this paper distinct prior distributions are derived in a Bayesian inference of the two-parameters Gamma distribution. Noniformative priors, such as Jeffreys, reference, MDIP, Tibshirani and an innovative prior based on the copula approach are investigated. We show that the maximal data information prior provides in an improper posterior density and that the different choices of the parameter of interest lead to different reference priors in this case. Based on the simulated data sets, the Bayesian estimates and credible intervals for the unknown parameters are computed and the performance of the prior distributions are evaluated. The Bayesian analysis is conducted using the Markov Chain Monte Carlo (MCMC) methods to generate samples from the posterior distributions under the above priors.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Zootecnia - FCAV
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Pós-graduação em Ciência Odontólogica - FOA
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Pós-graduação em Agronomia (Genética e Melhoramento de Plantas) - FCAV
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Pós-graduação em Ciência dos Materiais - FEIS
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Pós-graduação em Enfermagem - FMB
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.
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Weight records of Brazilian Nelore cattle, from birth to 630 d of age, recorded every 3 mo, were analyzed using random regression models. Independent variables were Legendre polynomials of age at recording. The model of analysis included contemporary groups as fixed effects and age of dam as a linear and quadratic covariable. Mean trends were modeled through a cubic regression on orthogonal polynomials of age. Up to four sets of random regression coefficients were fitted for animals' direct and maternal, additive genetic, and permanent environmental effects. Changes in measurement error variances with age were modeled through a variance function. Orders of polynomial fit from three to six were considered, resulting in up to 77 parameters to be estimated. Models fitting random regressions modeled the pattern of variances in the data adequately, with estimates similar to those from corresponding univariate analysis. Direct heritability estimates decreased after birth and tended to be lowest at ages at which maternal effect estimates tended to be highest. Maternal heritability estimates increased after birth to a peak around 110 to 120 d of age and decreased thereafter. Additive genetic direct correlation estimates between weights at standard ages (birth, weaning, yearling, and final weight) were moderate to high and maternal genetic and environmental correlations were consistently high.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)