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On the permutation entropy Bayesian estimation
(Elsevier Science, 2021-08)
We present the Bayesian estimation of Permutation Entropy. In particular, we studied the bias and the mean squared error in the entropy estimation when the length of the time series embedded in the m-dimension space is ...
Bayesian network semantics for Petri nets
(Elsevier Science, 2020-02)
Recent work by the authors equips Petri occurrence nets (PN) with probability distributions which fully replace nondeterminism. To avoid the so-called confusion problem, the construction imposes additional causal dependencies ...
The effect of factor interactions in Plackett-Burman experimental designs: Comparison of Bayesian-Gibbs analysis and genetic algorithms
(Elsevier Science, 2010-05)
A genetic algorithm has been developed in order to estimate not only the main effects but also the association of terms when analyzing the influence of experimental factors through a Plackett-Burman design of experiments. ...
Data Fusion through Fuzzy-Bayesian Networks for Belief Generation in Cognitive Agents
(Instituto de Informática - Universidade Federal do Rio Grande do Sul, 2019)
Bayesian inference for shape mixtures of skewed distributions, with application to regression analysisBAYESIAN ANALYSIS (ONLINE)
(INT SOC BAYESIAN ANALYSIS, 2016)
Bayesian inference for shape mixtures of skewed distributions, with application to regression analysisBAYESIAN ANALYSIS (ONLINE)
(INT SOC BAYESIAN ANALYSIS, 2016)
Semi-parametric bayesian inference for multi-season baseball dataBAYESIAN ANALYSIS (ONLINE)
(INT SOC BAYESIAN ANALYSIS, 2016)
Bayesian inference for shape mixtures of skewed distributions, with application to regression analysisBAYESIAN ANALYSIS (ONLINE)
(INT SOC BAYESIAN ANALYSIS, 2016)
Bayesian inference for shape mixtures of skewed distributions, with application to regression analysisBAYESIAN ANALYSIS (ONLINE)
(INT SOC BAYESIAN ANALYSIS, 2016)
Left ventricle segmentation using a Bayesian approach with distance dependent shape priors
(IOP Publishing, 2020-05-28)
We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to ...