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Flashcard 3573863288076

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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Annotation 4610456423692

#reading
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.
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Annotation 4653209226508

For count data, a probabilistic forecast is a predictive probability distribution, P, on the set of the nonnegative integers.
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Annotation 4654381534476

Strict propriety ensures that both calibration and sharpness are being addressed by the prediction (Winkler, 1996).
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Annotation 4659037474060

יקלח ןיד קספ לע רערעל ןתינ אליפוסה ןידה קספ לע רוערעה תרגסמב 3 795 ךשמהב הילע רערעל ןתינ אל )תושרב( הירחאל דימ םיכילה בוכיע רבדב הטלחה לע רוערע שגוה אל םא
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Flashcard 4659078368524

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

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

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Flashcard 4659079941388

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

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

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Flashcard 4659080989964

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

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

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Flashcard 4659085708556

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)

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

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Flashcard 4659086757132

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

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

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Flashcard 4659087805708

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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
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Gneiting, Balabdaoui, and Raftery (2007) contend that the goal of probabilistic forecasting is to maximize the sharpness of the predictive distributions subject to calibration.

Original toplevel document (pdf)

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Flashcard 4659092524300

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

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

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Flashcard 4659093572876

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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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

Original toplevel document (pdf)

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Annotation 4659098029324

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

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Annotation 4659099602188

#reading
In classical statistics, inferences are made utilising the distribution fX(x|θ) even after the data x has been observed
statusnot read reprioritisations
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started reading on finished reading on


Parent (intermediate) annotation

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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.

Original toplevel document (pdf)

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Annotation 4659101961484

#reading
In Bayesian statistics, parameters are treated as random and so may be equipped with a probability distribution
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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>

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Annotation 4659158584588


 #has-images

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Annotation 4659163041036

#has-images

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Annotation 4659181653260


 #has-images
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