Total no. of outcomes when two dice are rolled = 36
No. of outcome when 6 will be on both dice = 1
Probability of rolling two 6's = 1/36
Hope this helps!
A because the graph of a direct proportion crosses the origin.
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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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Answer:
D. 30 sq in.
Step-by-step explanation:
10+9+6+5=30 sq in.
Answer:
16
Step-by-step explanation:
solve for X.
16+4x=10+14
16+4x=24
4x=24-16
4x=8
x=2
8x=8(2)
8x=16
