Problem 1.0 For the gamblers ruin problem, let Ma denote the mean number of games that...
Exercise 7.1 (Gamblers ruin). Let (Xt) 120 be the Gambler's chain on state space Ω = {0, 1,2, , N} (i) Show that any distribution r-[a,0,0, ,0, bl on 2 is stationary with respect to the gambler?s (ii) Clearly the gambler's chain eventually visits state 0 or N, and stays at that boundary state introduced in Example 1.1. chain. Also show that any stationary distribution of this chain should be of this form. thereafter. This is called absorbtion. Let Ti...
1. Consider the following "Gambler's Ruin" problem. A gambler starts with a certain number of dollar bills between 1 and 5. During each turn of the game, there is a .55 chance that the gambler wil win a dollar, and a .45 chance that the gamble will lose a dollar. The game ends when the gambler has either S0 or S6. Let Xn represent the amount of money that the gambler has after turn n. (a) Give the one-step transition...
Gambler’s Ruin. A gambler, player A, plays a sequence of games against an opponent, player B. In each game, the probability of player A winning is p. If player A wins, he wins $1 which is paid by player B. If he loses a hand with probability q = 1-p, he must pay $1 to player B. The game ends either player B wins all the money from player A, and he is “ruined,” or when player A wins all...
Suppose in the gambler's ruin problem that the probability of winning a bet de- pends on the gambler's present fortune. Specifically, suppose that ai is the prob- ability that the gambler wins a bet when his or her fortune is i. Given that the gambler's initial fortune is i, let P(i) denote the probability that the gambler's fortune reaches N before 0. (a) Derive a formula that relates Pi) to Pi -1 and Pi 1) (b) Using the same approach...
Problem 1 Let Xi, ,Xn be a random sample from a Normal distribution with mean μ and variance 1.e Answer the following questions for 8 points total (a) Derive the moment generating function of the distribution. (1 point). Hint: use the fact that PDF of a density always integrates to 1. (b) Show that the mean of the distribution is u (proof needed). (1 point) (c) Using random sample X1, ,Xn to derive the maximum likelihood estimator of μ (2...