Let P be some probability measure on sample space S = [0, 1]. (a) Prove that we must have limn→∞ P((0, 1/n) = 0. (b) Show by example that we might have limn→∞ P ([0, 1/n)) > 0.
Let P be some probability measure on sample space S = [0, 1]. (a) Prove that...
5. Consider the sample space Ω = [0, 1]. Let P be a probability function such that for any interval fa, b, P(a, b-b-a. In other words, probabilty of any interval is its length Let us start with Co [0, 1, and at nth step, we define Cn by removing an interval of length 1/3 from the middle of each interval in Cn-1 For example, C1-[0, 1/3 u [2/3,1], C2-[0,1/9)U[2/9,1/3 U [2/3,7/9 U[8/9, 1] and so on. Here is a...
Consider the sample space Ω-10, 1]. Let P be a probability function such that for any interval [a, b], P([a, b) b- a. In other words, probabilty of any interval is its length. Let us start with Co 10, 1], and at nth step, we define C, by removing an interval of length 1/3° from the middle of each interval in Cn-1. For example, G = [0, 1/3ju [2/3, 11, c2 [0, 1/9] U [2/9, 1/3] U [2/3,7/9] U [8/9,...
2·Let Ω be a sample space and P be a probability. Prove that there can't exist events E, F that satisfy
Consider the probability space ([0, 1], B, IP), where P is uniform measure. Let X nlo,i/n). Determine which of the following statements hold. In each case, use the appropriate definition to verify your answer (a) E(X,] → 0 as n → oo (b) Xn →d 0 as n → oo (c) Xn, 0 as noo Consider the probability space ([0, 1], B, IP), where P is uniform measure. Let X nlo,i/n). Determine which of the following statements hold. In each...
(1) Let (2, A, i) be a measure space {AnE A E A} is a (a) Fix E E A. Prove that Ap 0-algebra of E, contained in A. (b) Let /i be the restriction of /u to Ap. Prove that ip is a measure on Ap. (c) Suppose that f : O -» R* is measurable (with respect to A). Let g the restriction of f to E. Prove that g : E -> R* is measurable (with respect...
Suppose Xn is a Markov chain on the state space S with transition probability p. Let Yn be an independent copy of the Markov chain with transition probability p, and define Zn := (Xn, Yn). a) Prove that Zn is a Markov chain on the state space S_hat := S × S with transition probability p_hat : S_hat × S_hat → [0, 1] given by p_hat((x1, y1), (x2, y2)) := p(x1, x2)p(y1, y2). b) Prove that if π is a...
Problem 1. Let A event from outcome space S,equipped with probability function I . Prove that P(A) 1. Hint: You can use theorem 1.4 Problem 2. Let A,B,C events from outcome space S, equipped with probability function P. Prove that P(AUBUC)- P(A)+P(B)+P(C)- PAnB) PAnC) PBnC) +P(AnBnc) Hint: You can treat A and BUC as two events and apply theorem 1.6. You will also need to use Law 5 from the distributive laws.
Problem 1. Let A event from outcome space S,equipped with probability function I . Prove that P(A) 1. Hint: You can use theorem 1.4 Problem 2. Let A,B,C events from outcome space S, equipped with probability function P. Prove that P(AUBUC)- P(A)+P(B)+P(C)- PAnB) PAnC) PBnC) +P(AnBnc) Hint: You can treat A and BUC as two events and apply theorem 1.6. You will also need to use Law 5 from the distributive laws.
3. Let A, B, C be events in a sample space S. Prove that (a) P(AUB) P(A)P(B), (b) P(AUBUC) P(A)+P(B)+P(C)-P(AnB)-P(Anc)-P(Bnc)+P(AnBnc)
6. Let si = 4 and sn +1 (sn +-) for n > 0. Prove lim n→oo sn exists and find limn-oo Sn. (Hint: First use induction to show sn 2 2 and the.show (sn) is decreasing)