Search results with tag "Gambler s ruin"
1 Stopping Times - Columbia University
www.columbia.edu1. (First passage/hitting times/Gambler’s ruin problem:) Suppose that X has a discrete state space and let ibe a xed state. Let ˝= minfn 0 : X n= ig: This is called the rst passage time of the process into state i. Also called the hitting time of the process to state i. More generally we can let Abe a collection of states such
ONE-DIMENSIONAL RANDOM WALKS
galton.uchicago.eduGambler’s Ruin. Simple random walk describes (among other things) the fluctuations in a speculator’s wealth when he/she is fully invested in a risky asset whose value jumps by either 1 in each time period. Although this seems far too simple a model to be of any practical value,
1 Gambler’s Ruin Problem - Columbia University
www.columbia.edu1 Gambler’s Ruin Problem Consider a gambler who starts with an initial fortune of $1 and then on each successive gamble either wins $1 or loses $1 independent of the past with probabilities p and q = 1−p respectively. Let R n denote the total fortune after the nth gamble. The gambler’s objective is to reach a total
Chapter 1 Markov Chains - Yale University
www.stat.yale.eduGambler’s Ruin. Consider a Markov chain on S= ... In this case, the outcome of the game depends on the Gambler’s fortune. When the fortune is i, the Gambler either wins or loses $1 with respective probabilities p i or q i, or breaks even (the fortune does not change) with probability r i. Another interpretation is that the state of the ...
Probability - University of Cambridge
www.statslab.cam.ac.ukexpectation. Random walks: gambler’s ruin, recurrence relations. Di erence equations and their solution. Mean time to absorption. Branching processes: generating functions and ex-tinction probability. Combinatorial applications of generating functions. [7] Continuous random variables: Distributions and density functions. Expectations; expec-
Chapter 6 - Random Processes - UAH
www.ece.uah.edurandom walk have been studied over the years (i.e., the gambler's ruin, drunken sailor, etc.). At first, a discrete random walk is introduced. Then, it is shown that a limiting form of the random walk is the well-known continuous Wiener process. Finally, simple equations are developed that
Essentials of Stochastic Processes - Duke University
services.math.duke.eduTo check this for the gambler’s ruin chain, we note that if you are still playing at time n, i.e., your fortune X n = i with 0 < i < N, then for any possible history of your wealth i n−1,i n−2,...i 1,i 0 P(X n+1 = i+1|X n = i,X n−1 = i n−1,...X 0 = i 0) = 0.4 since to increase your wealth by one unit you have to win your next bet. Here
Introduction to Stochastic Calculus - Duke University
services.math.duke.edui) The gambler’s ruin problem We play the following game: We start with 3$ in our pocket and we ip a coin. If the result is tail we loose one dollar, while if the result is positive we win one dollar. We stop when we have no money to bargain, or when we reach 9$. We may ask: what is the probability that I end up broke?
1 Gambler’s Ruin Problem - Columbia University
www.columbia.edu1.2 Applications Risk insurance business Consider an insurance company that earns $1 per day (from interest), but on each day, indepen-dent of the past, might su er a claim against it for the amount $2 with probability q= 1 p.
Random Walk: A Modern Introduction - University of Chicago
www.math.uchicago.edu4.6 Green’s function for a set 96 5 One-dimensional walks 103 5.1 Gambler’s ruin estimate 103 5.1.1 General case 106 5.2 One-dimensional killed walks 112 5.3 Hitting a half-line 115 6 Potential Theory 119 6.1 Introduction 119 6.2 Dirichlet problem 121 6.3 Difference estimates and Harnack inequality 125 6.4 Further estimates 132