Gaussian Maximum Likelihood
Mostrando 1-12 de 15 artigos, teses e dissertações.
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1. SPATIAL VARIABILITY OF SOYBEAN YIELD THROUGH A REPARAMETERIZED T-STUDENT MODEL
ABSTRACT: The t-Student distribution has been used to the spatial dependence modelling of soybean yield as an alternative to the normal distribution, being used for data with heavier tails or discrepant values. However, a usual Student t-distribution does not allow direct comparisons of geostatistical methods with a normal distribution. The aim of this study
Eng. Agríc.. Publicado em: 2017-08
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2. Information geometric similarity measurement for near-random stochastic processes
We outline the information-theoretic differential geometry of gamma distributions, which contain exponential distributions as a special case, and log-gamma distributions. Our arguments support the opinion that these distributions have a natural role in representing departures from randomness, uniformity, and Gaussian behavior in stochastic processes. We show
Publicado em: 2011
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3. 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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4. Support vector machines na classificação de imagens hiperespectrais / Hyperspectral image classification with support vector machines
É de conhecimento geral que, em alguns casos, as classes são espectralmente muito similares e que não é possível separá-las usando dados convencionais em baixa dimensionalidade. Entretanto, estas classes podem ser separáveis com um alto grau de acurácia em espaço de alta dimensão. Por outro lado, classificação de dados em alta dimensionalidade po
Publicado em: 2009
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5. Teste de hipóteses sobre o espectro de freqüência, aplicado na manutenção preditiva de motores de indução
This project had begun with the aim of technical improvement and a predictive maintenance tool development for electrical machines diagnosis. Based on nonintrusive approach Motor Current Signature Analysis - MCSA for three-phase induction motor broken bars detection, it was developed a spectral analysis method able to increase accuracy and reliability, compa
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 21/08/2008
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6. SMOOTH TRANSITION LOGISTIC REGRESSION MODEL TREE / MODELO DE REGRESSÃO LOGÍSTICA COM TRANSIÇÃO SUAVE ESTRUTURADO POR ÁRVORE (STLR-TREE)
The main goal of this work is to adapt the STR-Tree model, which is the combination of a Smooth Transition with Regression model with Classi cation and Regression Tree (CART), in order to use it in Classification. Some changes were made in its structural form and in the estimation. Due to the fact we are doing binary dependent variables classification, is ne
Publicado em: 2008
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7. Spatial and temporal soil moisture behavior at a watershed of the Mantiqueira Range, Minas Gerais. / âCOMPORTAMENTO ESPACIAL E TEMPORAL DA UMIDADE DO SOLO NUMA BACIA HIDROGRÃFICA NA SERRA DA MANTIQUEIRA, MINAS GERAISâ.
The qualitative and quantitative evaluation of the use and exploration of water resources at a watershed has been relevant on environmental context. The soil moisture consists of an important hydrological variable that determine the evapotranspiration and surface runoff production. This work aimed to analyze the spatial and temporal behavior of the soil mois
Publicado em: 2008
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8. Mixture models in quantitative genetics and applications to animal breeding
Finite mixture models are helpful for uncovering heterogeneity due to hidden structure; for example, unknown major genes. The first part of this article gives examples and reviews quantitative genetics issues of continuous characters having a finite mixture of Gaussian components. The partition of variance in a mixture, the covariance between relatives under
Revista Brasileira de Zootecnia. Publicado em: 2007-07
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9. 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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10. VOLATILIDADE ESTOCÁSTICA VIA VEROSSIMILHANÇA DE MONTE CARLO: UM ESTUDO COMPARATIVO / STOCHASTIC VOLATILITY VIA MONTE CARLO LIKELIHOOD: A COMPARATIVE STUDY
This dissertation discusses the estimation of the Stochastic Volatility (SV)model using a Durbin &Koopman methodology called Monte Carlo Like-lihood (MCL). The conditional coverage of value at risk (VaR) of SV via MCL model was compared to the GARCH (1,1) model and to the SV model via Quasi Maximum Likelihood (QML) estimation. The models were extended to Gau
Publicado em: 2004
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11. Sistemas de adaptação ao locutor utilizando autovozes. / Speaker adaptation system using eigenvoices.
This present work describe two speaker adaptation technique, using a small amount of adaptation data, for a speech recognition system. These techniques are Maximum Likelihood Linear Regression (MLLR) and Eigenvoices. Both re-estimates the mean of a continuous density Hidden Markov Model system. MLLR technique estimates a set of linear transformations for mea
Publicado em: 2001
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12. BAYESIAN LEARNING FOR NEURAL NETWORKS / APRENDIZADO BAYESIANO PARA REDES NEURAIS
This dissertation investigates the Bayesianan Neural Networks, which is a new approach that merges the potencial of the artificial neural networks with the robust analytical analysis of the Bayesian Statistic. Typically, theconventional neural networks such as backpropagation, have good performance but presents problems of convergence, when enough data for t
Publicado em: 1999