Answer:
Part A:
The probability that all of the balls selected are white:

Part B:
The conditional probability that the die landed on 3 if all the balls selected are white:

Step-by-step explanation:
A is the event all balls are white.
D_i is the dice outcome.
Sine the die is fair:
for i∈{1,2,3,4,5,6}
In case of 10 black and 5 white balls:






Part A:
The probability that all of the balls selected are white:


Part B:
The conditional probability that the die landed on 3 if all the balls selected are white:
We have to find 
The data required is calculated above:

Its 12. Part a was 2 part b was 3
6×2=12
4×3=12
Answer:
I don't know where those answers are coming from, but I got:
23s - 145
The purpose of the tensor-on-tensor regression, which we examine, is to relate tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without being aware of its intrinsic rank beforehand.
By examining the impact of rank over-parameterization, we suggest the Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN) methods to address the problem of unknown rank. By demonstrating that RGD and RGN, respectively, converge linearly and quadratically to a statistically optimal estimate in both rank correctly-parameterized and over-parameterized scenarios, we offer the first convergence guarantee for the generic tensor-on-tensor regression. According to our theory, Riemannian optimization techniques automatically adjust to over-parameterization without requiring implementation changes.
Learn more about tensor-on-tensor here
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I don’t know about a ratio table but she makes 18 dollars per hour