Victor Dias de Oliveira

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This work develops a Bayesian approach to perform inference and prediction in Gaussian random fields based on spatial censored data. These type of data occur often in the earth sciences due either to limitations of the measuring device or particular features of the sampling process used to collect the data. Inference and prediction on the underlying(More)
Anti-slug control of multiphase risers involves stabilizing an open-loop unstable operating point. PID control is the preferred choice in the industry, but appropriate tuning is required for robustness. In this paper, we define PIDF as a PID with a low-pass filter on its derivative action where the low-pass filter is crucial for the dynamics. We compared a(More)
This paper presents the impact analysis of the hypervisor layer over database applications. The conclusions were reached by performing experiments with hybrid environments, composed by bare-metal servers and two types of hypervisors. The main objectives were to compare the behaviour of database applications and to determine the maximum service capacity of a(More)
Scientific computing often requires high performance computational resources to perform large scale simulations in order to achieve appropriate results. These demand have been addressed with dedicated High-Performance Parallel and Distributed Computing (HPDC) infrastructures, but nowadays, a new dimension has been added by Cloud model, which has gathered(More)
This work revisits a simple model for geostatistical count data and make explicit the assumptions under which the model is constructed. We review the parameter estimators and predictors for a latent that appear in the literature, and propose new estimators and predictors. Finally, we plan to carry a detailed simulation experiment to investigate and compare(More)
This work describes a Bayesian approach for model selection in Gaussian conditional autoregressive models and Gaussian simultaneous autoregressive models which are commonly used to describe spatial lattice data. The approach is aimed at situations when all competing models have the same mean structure, but differ on some aspects of their covariance(More)
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