Let P be the standard normal distribution, i.e., P is the proba-bility measure on R, B(R) given bydP(x) = 1√2πe− x2/2dx.Consider the random variablesfn(x) = (1 + x2) 1/ne^(x^2/n+2) x ∈ R, n ∈ N.Using the dominated convergence theorem, prove that the limitlimn→∞E(fn)exists and find it
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Let P be the standard
bility measure on R, B(R) given by
dP(x) = 1
√2π
e
− x2/2
dx.
Consider the random variables
fn(x) = (1 + x2) 1/n
e^(x^2/n+2)
x ∈ R, n ∈ N.
Using the dominated convergence theorem, prove that the limit
limn→∞
E(fn)
exists and find it
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- Let Y1.,Yn be random sample from a Gamma (a, ß), show 1/nE, Y? converges in probability %3D1 and find its limit.Let X be a continuous random variable with PDF 5x4 0Suppose Y is a continuous random variable drawn from the uniform distributionon the interval [3, 4], that is, Y ∼ Uniform([3, 4]). Conditioned on Y = y, a second randomvariable X is drawn from the uniform distribution on the interval [0, y]. What is fX(x), thepdf of X?The moment generating function of the random variable X is given by: M(t) = e^(12t+24.5t^2). a. How is X distributed? b. Write the complete pdf of X. c. Find P(X>28.8). d. Find P(1.5 < X ≤ 22.5). e. Find P(0.784 < (X-12)^2 < 188.209).If X, Y are normally distributed independent random variables, then show that W = 2X - Y is normally distributed. Do not use moment generating function, only use convolution formula.A random variable W has range RW = {1, 2, 3, . . . } and the probability mass function givenby PW(k) = 1/((k2^k)(ln(2))) for all k ∈ RW. Find the expected value E[W].Let X1, X2, ..., Xn be a sequence of independent and identically distributedrandom variables having the Exponential(λ) distribution, λ > 0,fXi(x) = λe−λx , x > 00 , otherwise(a) Show that the moment generating function mX(s) := E(e^sX) = λ/λ−s for s < λ;(b) Using (a) find the expected value E(Xi) and the variance Var(Xi).(c) Define the random variable Y = X1 + X2 +· · ·+ Xn. Find E(Y ), Var(Y ) and the moment generating function of Y .(d) Consider a random variable X having Gamma(α, λ) distribution,fX(x) = (λαxα-1/Γ(α)) e−λx , x > 00 , otherwiseShow that the moment generating function of the random variable X is mX(s) =λα 1/(λ−s)α for s < λ, where Γ(α) isΓ(α) = (integral from 0 to inifity ) xα−1e−xdx.(e) What is the probability distribution of Y given in (c)? Explain youranswer.Let X be the lifetime (measured in hours) of an electronic device. suppose thatX let X be a continuous random variable with pdf f(x) = k/x^n, 2000 ≤ x ≤ 10000(a) For n in general, find k. (1.5)(b) What is the probability that the device fails before 5000 hours have passed? Compare the resultsSuppose that two random variables X and Y have the joint PDFfXY(u, v) = {60(u^2)v u ≥ 0, v ≥ 0, and u + v ≤ 1, 0 otherwise.(a) Are X and Y independent?(b) What is the marginal distribution fX(t)?(c) What is Pr[X ≥ Y]? (To set up the right integral, it might help you to draw the rangeof (X, Y) in the uv-plane and identify the region within that range where u ≥ v.)SEE MORE QUESTIONS
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