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status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Strict propriety ensures that both calibration and sharpness are being addressed by the prediction (Winkler, 1996).

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Strict propriety ensures that both calibration and sharpness are being addressed by the prediction (Winkler, 1996).

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Strict propriety ensures that both calibration and sharpness are being addressed by the prediction (Winkler, 1996).

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Gneiting, Balabdaoui, and Raftery (2007) contend that the goal of probabilistic forecasting is to maximize the sharpness of the predictive distributions subject to calibration.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Gneiting, Balabdaoui, and Raftery (2007) contend that the goal of probabilistic forecasting is to maximize the sharpness of the predictive distributions subject to calibration.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Gneiting, Balabdaoui, and Raftery (2007) contend that the goal of probabilistic forecasting is to maximize the sharpness of the predictive distributions subject to calibration.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

For count data, a probabilistic forecast is a predictive probability distribution, P, on the set of the nonnegative integers.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

For count data, a probabilistic forecast is a predictive probability distribution, P, on the set of the nonnegative integers.

status | not read | reprioritisations | ||
---|---|---|---|---|

last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

In classical statistics, the parameter is viewed as a fixed unknown constant and inferences are made utilising the distribution fX(x |θ) even after the data x has been observed. Conversely, in a Bayesian approach parameters are treated as random and so may be eq

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

In classical statistics, the parameter is viewed as a fixed unknown constant and inferences are made utilising the distribution fX(x |θ) even after the data x has been observed. Conversely, in a Bayesian approach parameters are treated as random and so may be equipped with a probability distribution.

status | not read | reprioritisations | ||
---|---|---|---|---|

last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

an> In classical statistics, the parameter is viewed as a fixed unknown constant and inferences are made utilising the distribution fX(x |θ) even after the data x has been observed. Conversely, <span>in a Bayesian approach parameters are treated as random and so may be equipped with a probability distribution. <span>

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

status | not read | reprioritisations | ||
---|---|---|---|---|

last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |