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- W2951158058 abstract "We analyze a new algorithm for probability forecasting of binary observations on the basis of the available data, without making any assumptions about the way the observations are generated. The algorithm is shown to be well calibrated and to have good resolution for long enough sequences of observations and for a suitable choice of its parameter, a kernel on the Cartesian product of the forecast space $[0,1]$ and the data space. Our main results are non-asymptotic: we establish explicit inequalities, shown to be tight, for the performance of the algorithm." @default.
- W2951158058 created "2019-06-27" @default.
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- W2951158058 date "2005-01-01" @default.
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- W2951158058 title "Non-asymptotic Calibration and Resolution" @default.
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- W2951158058 doi "https://doi.org/10.1007/11564089_33" @default.
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