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The purpose of this study was to determine whether the hyperbolic relationship between power output and time to exhaustion (work − time and power − [1/time] models) could be estimated from a modified version of a three-minute all-out rowing test (3-min RT), and to investigate the test–retest reliability of the 3-min RT. Eighteen male rowers volunteered to(More)
Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP(More)
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, π, for covariance function parameters requires computation of the covariance matrix, C, a pn 2 operation, where p is the number of covariates and n is the number of(More)
OBJECTIVE To understand correlative elements of post stroke depression (PSD). METHODS Record scores of the Hamilton Rating Scale for Depression (HRSD) for each PSD patient. Assess the degree of neurological deficit by the modified Scandinavian Stroke Scale (MSSS). Then analyze the correlativity of the patient's HRSD with MSSS, and the relation of PSD with(More)
2014 Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this thesis, we propose a GP(More)