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Now showing items 11-20 of 22
Scalable and interpretable kernel methods based on random Fourier features
(Universidade Federal de São Carlos, 2023-03-29)
Kernel methods are a class of statistical machine learning models based on positive semidefinite kernels, which serve as a measure of similarity between data features. Examples of kernel methods include kernel ridge ...
Seleção de covariância para o modelo grafo gaussiano via reversible jump
(Universidade Federal de São Carlos, 2023-02-24)
The purpose of the Graphical Gaussian model is to find the covariance structure that represents the relationship between random variables, whose joint distribution is a multivariate normal. This is a tool used to modeling ...
Bayesian estimation of dynamic mixture models by wavelets
(Universidade Federal de São Carlos, 2023-04-20)
Gaussian mixture models are used successfully in various statistical learning applications. The good results provided by these models encourage several generalizations of them. Among possible adaptations, one can assume a ...
Teoremas limite para variáveis aleatórias de Bernoulli dependentes
(Universidade Federal de São Carlos, 2023-03-22)
In this work, we consider a sequence of correlated Bernoulli variables whose probability of success for the current trial depends conditionally on previous trials. This conditional probability is given as a linear function ...
Inferência em redes aleatórias com pesos discretos
(Universidade Federal de São Carlos, 2023-04-04)
Random networks have been widely used to describe interactions between objects, including interpersonal relationships between individuals. One of the most important features of networks is the presence of communities, which ...
Propagação de rumor em uma população cética em N
(Universidade Federal de São Carlos, 2023-03-30)
We consider two models for information propagation in N. In both models, the individuals (one per site of N) have random, independent, and equally distributed radius. At the beginning only the individual at 0 has the ...
Small and time-efficient distribution-free predictive regions
(Universidade Federal de São Carlos, 2023-05-02)
Predicting a target variable (response) is often the main objective of many studies and investigations. In such scenarios, there are usually other variables, known as covariates, that are more readily available and can ...
Inferência Bayesiana para modelos de volatilidade estocástica baseados em mistura de escala da distribuição normal assimétrica
(Universidade Federal de São Carlos, 2023-02-28)
This dissertation aims to evaluate and compare the performance of the No-U-Turn Sampler
(NUTS) algorithm, implemented in the Stan software, in estimating the parameters of stochastic
volatility models with leverage based ...
Modelos Lomax assimétricos: uma nova abordagem para a classificação de dados binários desbalanceados
(Universidade Federal de São Carlos, 2023-05-17)
Imbalanced data refers to a dataset where one class has significantly fewer observations than the other class. This can lead to poor performance of both machine learning algorithms and statistical models, since most of ...
Diagnóstico e seleção de modelos com resposta binária e função de ligação assimétrica
(Universidade Federal de São Carlos, 2023-12-06)
For binary response variables, probit and logit link functions are widely used. However, when the data is imbalanced, traditional approaches may not be suitable. In this thesis, we consider the skew-probit link function ...