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- Question 1 of 24
1. Question
Erlang Distribution is also called General___________.
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2. Question
Distributions are the only discrete memoryless random distributions.
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3. Question
When can we use Gamma Distribution?
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 4 of 24
4. Question
A list of vectors is said to be linearly dependent if and only if there is no vector in the list which is in the span of the preceding vectors.
CorrectIncorrect - Question 5 of 24
5. Question
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 6 of 24
6. Question
Write down the conditions for the Poisson distribution to be valid.
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 7 of 24
7. Question
The successive search directions in the steepest-descent methods may not relate to each other.
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8. Question
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 9 of 24
9. Question
The sign of covariance shows the tendency of __________relationship between the variables
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10. Question
_____________method is used to accelerate the gradient descent algorithm by taking into consideration the exponentially weighted average of the gradients.
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11. Question
The Variance is a measure of dispersion that checks how ____________ apart the data in a distribution are from the mean.
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12. Question
A list of vectors is said to be linearly independent if and only if there is single vector in the list which is in the span of the preceding vectors.
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13. Question
A fair six sided die is rolled with X being the number on the uppermost face, the variance of X is _________
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14. Question
_______________converges slightly faster than the bisection method.
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15. Question
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 16 of 24
16. Question
an eigenvector is a vector whose direction remains __________when a linear transformation is applied to it
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17. Question
Sample_____________ is a measure of the tailedness of the probability distribution.
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18. Question
Define a matrix and compute its inverse and transpose in kotlin S2
Similar Expected Output:
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 19 of 24
19. Question
In kotlin S2 Determine whether is a basis for .
Expected Similar Output:
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 20 of 24
20. Question
Perform double exponential ,real line, half real line in kotlin S2.
Expected Similar Output:
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 21 of 24
21. Question
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 22 of 24
22. Question
Implement the polar decomposition using Kotlin S2.
Expected Similar Output:
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 23 of 24
23. Question
generate standard normal samples and normal samples based on a specified mean μ and variance
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted. - Question 24 of 24
24. Question
reduce this given matrix to the row echelon form and also find the eigen pair using kotlin S2.
Expected Similar Output:
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Grading can be reviewed and adjusted.Grading can be reviewed and adjusted.