13. When the occurrence of one event has no effect on the probability of the occurrence of another event, the events are called: a) Independent e b) Dependent e c) Mutually exclusive d) Equally likely
13. When the occurrence of one event has no effect on the probability of the occurrence of another event, the events are called: a) Independent e b) Dependent e c) Mutually exclusive d) Equally likely
Elementary Linear Algebra (MindTap Course List)
8th Edition
ISBN:9781305658004
Author:Ron Larson
Publisher:Ron Larson
Chapter2: Matrices
Section2.5: Markov Chain
Problem 55E
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solve question 13 with complete explation
![12. The events having no experimental outcomes in common is called:
a) Equally likely events e
b) Exhaustive events
c) Mutually exclusive events e
d) Independent events
13. When the occurrence of one event has no effect on the probability of the
occurrence of another event, the events are called:
a) Independent e
b) Dependent e
c) Mutually exclusive e
d) Equally likelye
14. The probability density function of a Markov process is
a) p(x1,x2,x3..xn) = p(x1)p(x2/x1)p(x3/x2)...p(xn/xn-1)e
b) p(x1,x2,x3.xn) = p(x1)p(x1/x2)p(x2/x3)..p(xn-1/xn)-
c) p(x1,x2,x3..xn) = p(x1)p(x2)p(x3)..p(xn)e
d) p(x1,x2,x3...xn) = p(x1)p(x2 *x1)p(x3*x2)...p(xn*xn-1)e
..xn) =
.....
15. The discrete probability distribution in which the outcome is very small with a
very small period of time is classified as «
a) Posterior distribution
b) Cumulative distributione
c) Normal distributione
d) Poisson distributione
16. In a Poisson Distribution, the mean and variance are equal.
a) True
b) False](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb0a2366f-9f57-4005-a997-19009e2a7d08%2F00d4355e-086b-4280-9fa2-ab758087bfd2%2Fgg8m9ai_processed.png&w=3840&q=75)
Transcribed Image Text:12. The events having no experimental outcomes in common is called:
a) Equally likely events e
b) Exhaustive events
c) Mutually exclusive events e
d) Independent events
13. When the occurrence of one event has no effect on the probability of the
occurrence of another event, the events are called:
a) Independent e
b) Dependent e
c) Mutually exclusive e
d) Equally likelye
14. The probability density function of a Markov process is
a) p(x1,x2,x3..xn) = p(x1)p(x2/x1)p(x3/x2)...p(xn/xn-1)e
b) p(x1,x2,x3.xn) = p(x1)p(x1/x2)p(x2/x3)..p(xn-1/xn)-
c) p(x1,x2,x3..xn) = p(x1)p(x2)p(x3)..p(xn)e
d) p(x1,x2,x3...xn) = p(x1)p(x2 *x1)p(x3*x2)...p(xn*xn-1)e
..xn) =
.....
15. The discrete probability distribution in which the outcome is very small with a
very small period of time is classified as «
a) Posterior distribution
b) Cumulative distributione
c) Normal distributione
d) Poisson distributione
16. In a Poisson Distribution, the mean and variance are equal.
a) True
b) False
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