VASAVI COLLEGE OF ENGINEERING (Autonomous), HYDERABAD
B.E. (ECE) VI Semester
Probability Theory and Stochastic Processes (Open Elective-II)
Time: 3 hours Max. Marks: 60
Note: Answer all questions from Part A and Part B.
Part A (10 x 2 = 20 Marks)
- Define conditional probability.
- State Baye's theorem.
- Define moment generating function.
- Define marginal distribution function.
- Define stochastic process.
- Define stationary process.
- Define autocorrelation function.
- Define ergodicity.
- Write any two properties of power spectral density.
- Define white noise.
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Part B (4 x 10 = 40 Marks)
- a) In a binary communication system, a ‘1’ or ‘0’ is transmitted. Because of noise in the system, a transmitted ‘1’ is sometimes received as a ‘0’, and a transmitted ‘0’ is sometimes received as a ‘1’. Let p denote the probability that a transmitted ‘1’ is received as a ‘1’, and let q denote the probability that a transmitted ‘0’ is received as a ‘0’. Suppose that the probability that a ‘1’ is transmitted is a.
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i) Find the probability that a ‘1’ is received.
ii) Find the probability that a ‘1’ was transmitted given that a ‘1’ was received.
OR
b) Define probability density function. Write the properties of Gaussian random variable. - a) Two random variables X and Y have joint density
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fXY(x,y) =K(x+y), & 0 < x < 1, 0 < y < 1 \\ 0, & Otherwise
Find
i) The value of K
ii) Marginal densities of X and Y
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b) The joint density function of two random variables X and Y is given by
fXY(x,y) = e-(x+y), x = 0, y = 0
Find the density function of the random variable Z = X/Y. - a) Define Poisson random process and derive the probability distribution function of Poisson random process.
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b) A random process is given by X(t) = A cos(?t + ?), where A and ? are constants and ? is a uniform random variable over (0, 2p). Show that X(t) is a wide sense stationary process. - a) Find the power spectral density of the response of a linear time-invariant system whose input is a WSS process.
OR
b) Derive the relation between the power spectral density and autocorrelation function.
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