space. (Bift>0 Be a ,r), and let (Stft>0 be the filtration generated by {Bt}t>0• Question 3: Let o be defined by 5, te (0, 1) Bo.5, te [1,2) te (2,3) t2 3 B2, 0, Let Y be given by Y = Oi dB and let V[Y] denote the variance of Y. Which of the following is correct? a) V[Y] = 7.5 b) V[Y] = 0 %3D c) V[Y] = 27.5 d) V[Y] = 56.25 %3D e) I don't know.
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- Theorem A at Section 8.8.1 saysA necessary and sufficient condition for T(X1,..., Xn) to be sufficient for a parameter θ is that the joint probability function (density function or frequency function) factors in the formf(x1,...,xn|θ) = g[T(x1,...,xn),θ]h(x1,...,xn)The probability of the closing of the ith relay in the circuits shown is given by p_i. Let p_1 = 0.4, p_2 = 0.1, p_3 = 0.5, p_4 = 0.7, p_5 = 0.6. If all relays function independently. what is the probability that a current flows between A and B for the respective circuit?Let U₁,..., Un be i.i.d. random variables uniformly distributed in [0, 1] and let Mn = max Ui . 1<i<n Find the cdf of Mn, which we denote by G (t), for t = [0, 1]. For t = [0, 1], G (t) = Now, let Fn (t) denote the cdf of n (1 - Mn); for t > 0, compute lim F₁ (t) =
- Let X = (X1, X2, ..., Xn)T be a random vector with mean vector μ and covariance matrix Σ. Suppose that for every a = (a1, a2, ..., an)T ∈ Rn, the random variable aTX has a (one-dimensional) Gaussian distribution on R. a) For fixed a ∈ Rn, compute the moment generating function MaTX(u) of the random variable aTX, writing the answer using μ & Σ. b) Define MX(t1, t2, ... , tn), the moment generating function of the random vector X, and express it in terms of MtTX, the moment generating function of tTX where t = (t1, t2, ..., tn). c) Combining a) and b), compute the moment generating function MX(t) of X and hence prove that the random vector X has a Gaussian distribution.4 (b) An insurance company provides customers with both auto and home insurance policies. For a particular customer, Χ is the deduction on his or her auto policy and Y is the deduction on the home policy. Possible values of Χ are K100 and K250, and for Y are K0, K100 and K200. The joint probability density function for ( ) ,YΧ is given by the following table: Χ Y K100 K250 K0 0.20 0.05 K100 0.10 0.15 K200 0.20 0.30 iv. If we look only at those insurance customers selecting the lowest auto mobile insurance deduction (K100), what is the probability that a randomly selected\ customer will also select the lowest home deduction (K0). v. Compute the correlation coefficient of Χ and YLet X be a discrete random variable with possible values {0,1,2,...} and the following probability mass function:P(X=0) = 4/5 and for k belongs to {1,2,3,...},P(X=k) = 1/10 * (2/3)^k. (a) Verify that the above is a probability mass function (b) For k belongs tp {1,2,...}, find P(X>=k | X >=1)
- Prove that for a continuous random variable X,E (aX+ b) = aE (X) + b.Let {Z(t),t≥0} be a birth and death process on {0, 1, 2,3} with respective birth and death parameters λn=6−2n and μn=3n ; n = 0, 1, 2,3. Let Pn(t) be the probability that the process is in state n at time t.The steady state probabilities of the process are(round off answers to two decimal points); P0 Answer P1 Answer P2 Answer P3 Answersuppose that the probability density function of x is f(x)={3x^2, 0, 0<x<1 elsewhere. determine p(x<1/3), P(1/3 <=x<2/3), and P(x=>2/3)
- i) Let X and Y be integrable random variables on the probability space (Ω,F,P)andA be a sub-σ-field of F. Show that (i) if X ≤ Y a.s., then E(X|A) ≤ E(Y|A) a.s.; (ii) if a and b are constants, then E(aX + bY|A)=aE(X|A)+bE(X|A)Suppose that {Xn, n ≥ 1} is a sequence of discrete random variables with distribution functionP(Xn =i/n) = 2i/(n(n + 1)), for i = 1, 2, ..., n.Let X be a continuous random variable with density functionfX(x) = 2x for 0 < x < 1,0 , otherwise(a) Compute P(X ≤ x) for 0 < x < 1;(b) Compute P(Xn ≤m/n) for some integer m such that 1 ≤ m ≤ n;(c) Compute P(Xn ≤ x) for 0 < x < 1;(d) Prove that Xnd→ X.Suppose the random vector X = (X1, X2, X3, X4)^T moment generating function. ((X1, X2, X3, X4)^T <- T is Transpose) Mx(t1,t2,t3,t4) = (1-t2-4t1)^(-5) * (1-t3-t4)^(-4) Find the first two moments of X1, X2, X3, and X4. Are X1 and X2 independent? (X1, X2) and (X3, X4) are independent? (X1, X3) and (X2, X4) are independent?