Let W1, W2, . . . be an uncorrelated random sequence with mean 0 and variance 1.Define the discrete-time random process {Xn : n ∈ N} := {X1, X2, . . .} by Xn = aXn−1 + Wn(n ∈ N) with a and X0 given. For each of the following two separate cases, find the mean functionmX (n) (n ∈ N) and covariance function CX (m, n) (m, n ∈ N) for the process {Xn : n ∈ N}, anddetermine if it is wide-sense stationary.a. a = 1 and X0 = 0.b. |a| < 1 and X0 is a random variable with mean 0 and variance 1/(1 − a2), uncorrelated withW1, W2, . . . .
Let W1, W2, . . . be an uncorrelated random sequence with mean 0 and variance 1.Define the discrete-time random process {Xn : n ∈ N} := {X1, X2, . . .} by Xn = aXn−1 + Wn(n ∈ N) with a and X0 given. For each of the following two separate cases, find the mean functionmX (n) (n ∈ N) and covariance function CX (m, n) (m, n ∈ N) for the process {Xn : n ∈ N}, anddetermine if it is wide-sense stationary.a. a = 1 and X0 = 0.b. |a| < 1 and X0 is a random variable with mean 0 and variance 1/(1 − a2), uncorrelated withW1, W2, . . . .
Let W1, W2, . . . be an uncorrelated random sequence with mean 0 and variance 1.Define the discrete-time random process {Xn : n ∈ N} := {X1, X2, . . .} by Xn = aXn−1 + Wn(n ∈ N) with a and X0 given. For each of the following two separate cases, find the mean functionmX (n) (n ∈ N) and covariance function CX (m, n) (m, n ∈ N) for the process {Xn : n ∈ N}, anddetermine if it is wide-sense stationary.a. a = 1 and X0 = 0.b. |a| < 1 and X0 is a random variable with mean 0 and variance 1/(1 − a2), uncorrelated withW1, W2, . . . .
Let W1, W2, . . . be an uncorrelated random sequence with mean 0 and variance 1. Define the discrete-time random process {Xn : n ∈ N} := {X1, X2, . . .} by Xn = aXn−1 + Wn (n ∈ N) with a and X0 given. For each of the following two separate cases, find the mean function mX (n) (n ∈ N) and covariance function CX (m, n) (m, n ∈ N) for the process {Xn : n ∈ N}, and determine if it is wide-sense stationary. a. a = 1 and X0 = 0. b. |a| < 1 and X0 is a random variable with mean 0 and variance 1/(1 − a2), uncorrelated with W1, W2, . . . .
Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...
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