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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)« b) p(x1,x2,x3..xn) = p(x1)p(x1/x2)p(x2/x3......(xn-1/xn)- c) p(x1,x2,x3..xn) = p(x1)p(x2)p(x3)..p(xn)« d) p(x1,x2,x3...xn) = p(x1)p(x2 *x1)p(x3*x2)....(xn*xn-1)« %3D

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)« b) p(x1,x2,x3..xn) = p(x1)p(x1/x2)p(x2/x3......(xn-1/xn)- c) p(x1,x2,x3..xn) = p(x1)p(x2)p(x3)..p(xn)« d) p(x1,x2,x3...xn) = p(x1)p(x2 *x1)p(x3*x2)....(xn*xn-1)« %3D

Linear Algebra: A Modern Introduction
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter3: Matrices
Section3.7: Applications
Problem 12EQ: 12. Robots have been programmed to traverse the maze shown in Figure 3.28 and at each junction...
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solve question 14 with complete explanation typed
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
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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