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The aim of this article is to design a moment transformation for Student-t distributed random variables, which is able to account for the error in the numerically computed mean. We employ Student-t process quadrature, an instance of Bayesian quadrature, which allows us to treat the integral itself as a random variable whose variance provides information about the incurred integration error. Advantage...
The authors apply the nonparametric kernel predictor to the time-series prediction problem. Because nonparametric prediction makes few assumptions about the underlying time series, it is useful when modeling uncertainties are pervasive, such as when the time series is non-Gaussian. It is shown that the nonparametric kernel predictor is asymptotically optimal for bounded, mixing time series. Numerical...
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