Map Estimators
Mostrando 1-9 de 9 artigos, teses e dissertações.
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1. DELINEATION OF HOMOGENEOUS ZONES BASED ON GEOSTATISTICAL MODELS ROBUST TO OUTLIERS
RESUMO Diversas pesquisas utilizam medidas de condutividade elétrica aparente do solo (CEa) como indicador da variabilidade espacial de atributos físico-químicos existentes no campo de produção. Com base nestas medidas, zonas de manejo (ZM) são delineadas para aperfeiçoamento da gestão agrícola. Entretanto, estas amostras têm apresentado presença
Rev. Caatinga. Publicado em: 18/07/2019
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2. Slash Spatial Linear Modeling: Soybean Yield Variability as a Function of Soil Chemical Properties
ABSTRACT: In geostatistical modeling of soil chemical properties, one or more influential observations in a dataset may impair the construction of interpolation maps and their accuracy. An alternative to avoid the problem would be to use most robust models, based on distributions that have heavier tails. Therefore, this study proposes a spatial linear model
Rev. Bras. Ciênc. Solo. Publicado em: 15/02/2018
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3. GAUSSIAN SPATIAL LINEAR MODEL OF SOYBEAN YIELD USING BOOTSTRAP METHODS
ABSTRACT This study aims to quantify the uncertainties associated to the parameters of a Gaussian spatial linear model (GSLM) and the assumption of normality residuals in the modeling of the spatial dependence of the soybean yield as a function of soil chemical attributes. The spatial bootstrap methods were used to determine the point and interval estimators
Eng. Agríc.. Publicado em: 2018-01
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4. Novas propostas em filtragem de projeções tomográficas sob ruído Poisson
In this dissertation we present techniques for filtering of tomographic projections with Poisson noise. For the filtering of the tomogram projections we use variations of three filtering techniques: Bayesian estimation, Wiener filtering and thresholding in Wavelet domain. We used ten MAP estimators, each estimator with a diferent probability density as prior
Publicado em: 2010
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5. Combinação de modelos de campos aleatórios markovianos para classificação contextual de imagens multiespectrais / Combining markov random field models for multispectral image contextual classification
This work presents a novel MAP-MRF approach for multispectral image contextual classification by combining higher-order Markov Random Field models. The statistical modeling follows the Bayesian paradigm, with the definition of a multispectral Gaussian Markov Random Field model for the observations and a Potts MRF model to represent the a priori knowledge. In
Publicado em: 2010
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6. Contribuições da teoria da estimação para modulações digitais que utilizam sinais caóticos. / Contributions of the estimation theory to digital modulations that use chaotic signals.
In this work, we investigate the use of estimation techniques to digital modulation systems that use chaotic signals. Initially, basic aspects of nonlinear systems and digital modulation theory are reviewed followed by currently proposed techniques of chaotic digital modulation with coherent, noncoherent and differential correlation receivers: CSK (Chaos Shi
Publicado em: 2006
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7. Nonlinear dynamic modeling of multicomponent batch distillation: a case study
The aim of this work is to compare several of the commercial dynamic models for batch distillation available worldwide. In this context, BATCHFRAC™, CHEMCAD™ BATCH, and HYSYS.Plant® software performances are compared to experimental data. The software can be used as soft sensors, playing the roll of ad-hoc observers or estimators for control objectives.
Brazilian Journal of Chemical Engineering. Publicado em: 2002-07
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8. Genetic Architecture of Autosome-Mediated Hybrid Male Sterility in Drosophila
Several estimators have been developed for assesing the number of sterility factors in a chromosome based on the sizes of fertile and sterile introgressed fragments. Assuming that two factors are required for producing sterility, simulations show that one of these, twice the inverse of the relative size of the largest fertile fragment, provides good average
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9. Maximum Likelihood Estimation of Linkage and Interference from Tetrad Data
Maximum likelihood equations have been derived for estimation of map distance and interference from two-point and ranked tetrad data. The estimators have been applied to data from Saccharomyces cerevisiae and Schizosaccharomyces pombe. S. cerevisiae consistently shows quite strong interference over the mapped genome. In striking contrast, S. pombe consistent