ONE-DIMENSIONAL RANDOM WALKS
Gambler’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,
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Prologue - University of Chicago
galton.uchicago.eduLECTURE 5: BROWNIAN MOTION 1. Prologue We have seen in previous lectures that, for discrete multiperiod markets which admit no …
Bernoulli Distribution - University of Chicago
galton.uchicago.eduBernoulli Distribution Example: Toss of coin Deflne X = 1 if head comes up and X = 0 if tail comes up. Both realizations are equally likely: (X = 1) = (X = 0) = 1 2
A Review of Methods for Missing Data - University …
galton.uchicago.eduEducational Research and Evaluation 1380-3611/01/0704-353$16.00 2001, Vol. 7, No. 4, pp. 353–383 # Swets & Zeitlinger A Review of Methods for Missing Data …
Department of Statistics, University of Chicago
galton.uchicago.eduDepartment of Statistics, Columbia University PER A. MYKLAND Department of Statistics, University of Chicago We propose a methodology for evaluating the hedging errors of derivative securities due to the discreteness of trading times or the observation times of market prices, or
MARKOV CHAINS: BASIC THEORY - University of Chicago
galton.uchicago.eduMARKOV CHAINS: BASIC THEORY 3 Definition 2. A nonnegative matrix is a matrix with nonnegative entries. A stochastic matrix is a square nonnegative matrix all of whose row sums are 1. A substochastic matrix is a square nonnegative matrix all of whose row sums are 1.
CONVERGENCE RATES OF MARKOV CHAINS
galton.uchicago.eduMarkov chains for which the convergence rate is of particular interest: (1) the random-to-top shuffling model and (2) the Ehrenfest urn model. Along the way we will encounter a number of fundamental concepts and techniques, notably reversibility, total variation distance, and
Chapter 3. Multivariate Distributions.
galton.uchicago.edu3-1 Chapter 3. Multivariate Distributions. ... structure to include multivariate distributions, the probability distributions of pairs of random variables, triplets of random variables, and so forth. We will begin with the simplest such situation, that of pairs of ... describes a surface in 3-dimensional space, and the probability that (X;Y) ...
CONDITIONAL EXPECTATION AND MARTINGALES
galton.uchicago.educonditional expectations behave like ordinary expectations, with random quantities that are functions of the conditioning random variable being treated as constants.2 Let Y be a random variable, vector, or object valued in a measurable space, and let X be an integrable random variable (that is, a random variable with EjXj˙1).
BROWNIAN MOTION - Department of Statistics
galton.uchicago.eduMany stochastic processes behave, at least for long stretches of time, like random walks with small but frequent jumps. The argument above suggests that such processes will look, at least approximately, and on the appropriate time scale, like Brownian motion. Second, it suggests that many important “statistics” of the random walk will have lim-
CONDITIONAL EXPECTATION AND MARTINGALES
galton.uchicago.eduadapted sequence of integrable real-valued random variables, that is, a sequence with the prop-erty that for each n the random variable Xn is measurable relative to Fn and such that EjXnj˙ 1. The sequence X0,X1,... is said to be a martingale relative to the filtration {Fn}n‚0 if it is adapted and if for every n, (1) E(Xn¯1 jFn) ˘ Xn.
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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
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
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
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 $1 with respective probabilities p i or q i, or breaks even (the fortune does not change) with probability r i. Another interpretation is that the state of the ...
Probability - University of Cambridge
www.statslab.cam.ac.ukexpectation. Random walks: gambler’s ruin, recurrence relations. Di erence equations and their solution. Mean time to absorption. Branching processes: generating functions and ex-tinction probability. Combinatorial applications of generating functions. [7] Continuous random variables: Distributions and density functions. Expectations; expec-
Introduction to Stochastic Calculus - Duke University
services.math.duke.edui) The gambler’s ruin problem We play the following game: We start with 3$ 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?
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
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