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Tags

#asset-swap #finance #gale-using-and-tradning-asset-swaps

Question

side is the present value of the f

Answer

[default - edit me]

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scheduled repetition interval | last repetition or drill |

#reading

In classical statistics, the parameter is viewed as a fixed unknown constant and inferences are made utilising the distribution f_{X}(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.

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

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

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Question

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

Answer

Strict propriety

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 |

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

Question

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

Answer

calibration

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 |

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

Question

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

Answer

sharpness

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 |

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

Question

[...] contend that the goal of probabilistic forecasting is to maximize the sharpness of the predictive distributions subject to calibration.

Answer

Gneiting et al. (2007)

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 |

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

Question

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

Answer

maximize the sharpness

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 |

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

Question

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

Answer

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.

Question

For count data, a probabilistic forecast is a [...] on the set of the nonnegative integers.

Answer

predictive probability distribution, P,

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 |

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

Question

For count data, a probabilistic forecast is a predictive probability distribution, P, on [...].

Answer

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.

#reading

In classical statistics, the parameter is viewed as a fixed unknown constant

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

#reading

In classical statistics, inferences are made utilising the distribution f_{X}(x|θ) even after the data x has been observed

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 equipped with a probability distribution.

#reading

In Bayesian statistics, 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>

#has-images

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#has-images

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#has-images

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

started reading on | finished reading on |