116 resultados para Modeling Rapport Using Hidden Markov Models


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Pós-graduação em Engenharia Elétrica - FEIS

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Pós-graduação em Genética e Melhoramento Animal - FCAV

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Agronomia (Ciência do Solo) - FCAV

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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 Agronomia (Genética e Melhoramento de Plantas) - FCAV