Answer:
The inequality is:
6+4p>176+4p>176+ 4p > 17
Kim's team scored a minimum of 3 points per inning.
Step-by-step explanation:
;)
To find the average speed of the jet, we divide 4000 by 5. When we do so, we get 800. Therefore, the average speed of the jet was 800 kilometers per hour. Hope this helped!
It is 70° because the other triangle has the same angle and it is labeled 70° in the other triangle.
Answer: 70°
9514 1404 393
Answer:
Perimeter: 17 inches
Area: 8 square inches
Step-by-step explanation:
The ratio of perimeters is the same as the similarity ratio.
JKLM perimeter / ABCD perimeter = P/68 = 1/4
Multiplying by 68, we get ...
P = 68/4 = 17 . . . . inches
__
The ratio of areas is the square of the similarity ratio.
JKLM area / ABCD area = A/128 = (1/4)^2
Multiplying by 128, we get ...
A = 128/16 = 8 . . . . square inches
__
Perimeter = 17 inches
Area = 8 square inches
Distributionally robust stochastic programs with side information based on trimmings
This is a research paper whose authors are Adrián Esteban-Pérez and Juan M. Morales.
Abstract:
- We look at stochastic programmes that are conditional on some covariate information, where the only knowledge of the possible relationship between the unknown parameters and the covariates is a limited data sample of their joint distribution. We build a data-driven Distributionally Robust Optimization (DRO) framework to hedge the decision against the inherent error in the process of inferring conditional information from limited joint data by leveraging the close relationship between the notion of trimmings of a probability measure and the partial mass transportation problem.
- We demonstrate that our technique is computationally as tractable as the usual (no side information) Wasserstein-metric-based DRO and provides performance guarantees. Furthermore, our DRO framework may be easily applied to data-driven decision-making issues involving tainted samples. Finally, using a single-item newsvendor problem and a portfolio allocation problem with side information, the theoretical findings are presented.
Conclusions:
- We used the relationship between probability reductions and partial mass transit in this study to give a straightforward, yet powerful and creative technique to expand the usual Wasserstein-metric-based DRO to the situation of conditional stochastic programming. In the process of inferring the conditional probability measure of the random parameters from a limited sample drawn from the genuine joint data-generating distribution, our technique generates judgments that are distributionally resilient to uncertainty. In a series of numerical tests based on the single-item newsvendor issue and a portfolio allocation problem, we proved that our strategy achieves much higher out-of-sample performance than several current options. We backed up these actual findings with theoretical analysis, demonstrating that our strategy had appealing performance guarantees.
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