General Boundary Problem: System of Equations Solution

Similar documents
The Boundary Problem: Markov Chain Solution

The Geometric Random Walk: More Applications to Gambling

MARKOV CHAINS. Discussion attached. Source:

Math-Stat-491-Fall2014-Notes-V

Boundary Problems for One and Two Dimensional Random Walks

T 1. The value function v(x) is the expected net gain when using the optimal stopping time starting at state x:

1 Gambler s Ruin Problem

Math 2J Lecture 16-11/02/12


MATH 56A SPRING 2008 STOCHASTIC PROCESSES

STOCHASTIC MODELS LECTURE 1 MARKOV CHAINS. Nan Chen MSc Program in Financial Engineering The Chinese University of Hong Kong (ShenZhen) Sept.

Module 6:Random walks and related areas Lecture 24:Random woalk and other areas. The Lecture Contains: Random Walk.

MATH 307 Test 1 Study Guide

Lecture 4. David Aldous. 2 September David Aldous Lecture 4

Lecture 4 - Random walk, ruin problems and random processes

MATH 19B FINAL EXAM PROBABILITY REVIEW PROBLEMS SPRING, 2010

Inference for Stochastic Processes

Random Walks and Quantum Walks

Markov Chains (Part 3)

Chapter 11 Advanced Topic Stochastic Processes

SOLVING AND COMPUTING THE DISCRETE DIRICHLET PROBLEM

This operation is - associative A + (B + C) = (A + B) + C; - commutative A + B = B + A; - has a neutral element O + A = A, here O is the null matrix

Math Homework 5 Solutions

Gambler s Ruin with Catastrophes and Windfalls

1 Random Walks and Electrical Networks

Mathematical Games and Random Walks

Some properties of transition matrices for chain binomial models

33 The Gambler's Ruin 1

RECURSION EQUATION FOR

IEOR 6711: Professor Whitt. Introduction to Markov Chains

Math 180B Homework 4 Solutions

IE 336 Seat # Name (clearly) Closed book. One page of hand-written notes, front and back. No calculator. 60 minutes.

Some Definition and Example of Markov Chain

Adding and Subtracting Polynomials

Random walk on a polygon

6 Stationary Distributions

1 Gambler s Ruin Problem

Markov Chains and Martingales

Advanced Data Science

The Distribution of Mixing Times in Markov Chains

Handout 1: Introduction to Dynamic Programming. 1 Dynamic Programming: Introduction and Examples

CORE. Chapter 3: Interacting Linear Functions, Linear Systems. Algebra Assessments

The Geometric Distribution

Chapter 1. Vectors, Matrices, and Linear Spaces

RANDOM WALKS IN ONE DIMENSION

Math 250B Midterm II Information Spring 2019 SOLUTIONS TO PRACTICE PROBLEMS

The Chi-Square Distributions

V. RANDOM VARIABLES, PROBABILITY DISTRIBUTIONS, EXPECTED VALUE

(b). What is an expression for the exact value of P(X = 4)? 2. (a). Suppose that the moment generating function for X is M (t) = 2et +1 3

Random Walk and Other Lattice Models

The Chi-Square Distributions

Lectures on Probability and Statistical Models

A New Interpretation of Information Rate

Math 456: Mathematical Modeling. Tuesday, March 6th, 2018

6.842 Randomness and Computation March 3, Lecture 8

Math 3191 Applied Linear Algebra

Markov Chains CK eqns Classes Hitting times Rec./trans. Strong Markov Stat. distr. Reversibility * Markov Chains

15-780: LinearProgramming

Lecture 2 : CS6205 Advanced Modeling and Simulation

Discrete Event Systems Solution to Exercise Sheet 6

The Game of Normal Numbers

Unpredictable Walks on the Integers

Sequences, their sums and Induction

Activity 8: Eigenvectors and Linear Transformations

1 if the i-th toss is a Head (with probability p) ξ i = 0 if the i-th toss is a Tail (with probability 1 p)

Chapter 9: Systems of Equations and Inequalities

Discrete random structures whose limits are described by a PDE: 3 open problems

Two hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE. Date: Monday 22nd May 2017 Time: 09:45-11:45

Elementary Matrices. MATH 322, Linear Algebra I. J. Robert Buchanan. Spring Department of Mathematics

From the Temperley Lieb algebra to non-crossing partitions. V.S. Sunder joint work with Vijay Kodiyalam (both of IMSc, Chennai)

Convex Optimization CMU-10725

Homework set 2 - Solutions

Errata List for the Second Printing

2 DISCRETE-TIME MARKOV CHAINS

Random walks and electric networks

1 Normal Distribution.

Extrema and the First-Derivative Test

Chapter 29 out of 37 from Discrete Mathematics for Neophytes: Number Theory, Probability, Algorithms, and Other Stuff by J. M.

f (x) f (a) f (a) = lim x a f (a) x a

Complex Matrix Transformations

Math 115 First Midterm October 9, 2017

Runs of Identical Outcomes in a Sequence of Bernoulli Trials

. Find E(V ) and var(v ).

1. Introduction. 2. Outlines

ABSTRACT MARKOV CHAINS, RANDOM WALKS, AND CARD SHUFFLING. Nolan Outlaw. May 2015

Section 1.1 System of Linear Equations. Dr. Abdulla Eid. College of Science. MATHS 211: Linear Algebra

Eigenvalues and Eigenvectors

Interlude: Practice Final

Position, Velocity, Acceleration

1. In Activity 1-1, part 3, how do you think graph a will differ from graph b? 3. Draw your graph for Prediction 2-1 below:

Let's look at some higher order equations (cubic and quartic) that can also be solved by factoring.

Forces in Two Dimensions Teacher s Guide

PHYS102 Previous Exam Problems. Electric Fields

Algebra II Honors Unit 3 Assessment Review Quadratic Functions. Formula Box. f ( x) 2 x 3 25 from the parent graph of

Problem Points S C O R E Total: 120

OBJECTIVE Find limits of functions, if they exist, using numerical or graphical methods.

Stochastic processes and stopping time Exercises

CS 125 Section #12 (More) Probability and Randomized Algorithms 11/24/14. For random numbers X which only take on nonnegative integer values, E(X) =

MATH 56A: STOCHASTIC PROCESSES CHAPTER 1

Mon Feb Matrix algebra and matrix inverses. Announcements: Warm-up Exercise:

Transcription:

MATH 54 General Boundary Problem: System of Equations Solution Consider again the scenario of a random walk X that starts at positive height j, and on each independent step, moves upward a units with probability p, moves downward b units with probability q, or remains at the same height with probability r = 1 p q It stops upon reaching any height greater than or equal to n or dropping to any height less than or equal to m (We again assume that all height units are integers) We now will use a system of linear equations that gives an exact simultaneous solution for the probability of reaching the upper boundary before the lower boundary for all starting heights j In addition, we can easily adjust the system to find the average number of steps needed to hit a boundary However, the solutions will still be numerical rather than closed-form algebraic formulas The System of Equations Let x j be the probability of reaching the upper boundary first when starting at height j Then we know that x m = and x n = 1 (because if we start at height m then we can't reach n first, and if we start at height n then we are certain to reach n first) In fact, in because we may surpass n or drop below m, then x j = for all j m, and x j = 1 for all j n Now consider the values of x j for m < j < n Assuming we go up a units at a time or down b units at a time, then x j can be written in terms of x j b and x j+ a by x j = q x j b + r x j + p x j+ a We can re-write this set of equations, for m < j < n, as q x j b + (r 1) x j + p x j+ a = The system of equations is completed with the equations x j = for j m, and x j = 1 for j n Example 1 Suppose we go up or down 1 unit at a time, where p = 4, q = 5, and r = 1 We quit if we reach height 8 or height (Here we do not specify an initial starting height) What are the probabilities of reaching 8 before when starting at each height of, 1, 2,, 8? Solution We let x j be the probability of reaching height 8 before height when starting at height j Then we have the system of equations:

x = q x + (r 1)x 1 + px 2 = qx 1 + (r 1)x 2 + px 3 = qx 2 + (r 1)x 3 + px 4 = x 8 = 1 or x = 5 x 9 x 1 + 4 x 2 = 5x 1 9x 2 + 4 x 3 = 5 x 2 9x 3 + 4 x 4 = x 8 = 1 Note that the matrix of coefficients matrix is nearly identical to the matrix of transition probabilities used in the Markov Chain method The only difference is that now there is a string of r 1 down the inner portion of the main diagonal rather than r If r =, then these terms are 1 We shall call this matrix of coefficients T Let F be the 9 1 column of constants Then we are solving the system T X = F 1 x 5 9 4 x 1 5 9 4 x 2 5 9 4 x 3 5 9 4 x 4 5 9 4 x 5 5 9 4 x 6 5 9 4 x 7 1 x 8 = 1 The matrix of coefficients T will always be invertible Here det(t) 325 Hence we can solve by X = T 1 F x x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 = X = T 1 F = 53985 113397 192144 29579 413622 567426 759681 1

Thus when starting at height 7, there is almost a 76% chance of reaching 8 before dropping to, but only a 29% chance when starting at height 4 Because the up/down movements are both 1 unit each, these values can be found 1 (q / p)j much more quickly with the known closed-form formula 8 for j 8 1 (q / p) However, this system of equations method is very useful when a b We also know a closed-form formula for the average number of steps to hit a boundary in this case: n E[ j S ] = n 1 (q / p) j p q 1 (q / p) n j p q Example 2 Suppose now we go up 3 units with probability p = 4, go down 2 units with probability q = 5, or stay constant with r = 1 We wish to reach a goal of 1 (or more) before dropping to (or below) (a) What are the probabilities of doing so when starting at height j, for 1 j 9? (b) What is the average number of steps needed to hit a boundary? Solution Here we can actually reach a height of 12 (if going up while at height 9) and can actually drop to 1 (if going down while at height 1) Thus, x j = for 1 j, and x j = 1 for 1 j 12 For 1 j 9, we have q x j 2 + (r 1) x j + p x j+3 = We thereby have a system T X = F of 14 equations and 14 unknowns with the following augmented matrix T F : T F 1 1 q r 1 p q r 1 p q r 1 p q r 1 p q r 1 p q r 1 p q r 1 p q r 1 p q r 1 p 1 1 1 1 1 1 The solution is given by X = T 1 F Below is the result from a Mathematica program that solves the system

12 1 11 1 1 1 9 873637 8 811138 7 772546 6 6648 5 59583 4 489177 3 414139 2 262481 1 217412 1 For each possible initial height j, we have the probability of reaching 1 or more before dropping to or less Average Number of Steps Suppose we want to find the average number of steps needed to hit either boundary of n or m To do so, we can create a similar system of equations Now let x j be the average number of steps when starting at height j Then we know x j = for all j m and for all j n Now consider the values of x j for m < j < n Assuming we go up a units at a time or down b units at a time, then x j can be written in terms of x j b and x j+ a First one step must be taken, then we start over at either ( j b), or at ( j + a), or still at j if we have remained constant Thus, x j = 1 + q x j b + r x j + p x j+ a The middle set of equations, for m < j < n, becomes q x j b + (r 1) x j + p x j+ a = 1 The system of equations is completed with the equations x j = for j m, and x j = for j n So the only changes required are in generating the matrix F with 1 in the middle terms and for j n The matrix of coefficients T remains the same

So in Example 2, the solution is X = T 1 F where 1 1 1 1 1 F = 1 1 1 1 With Mathematica, we obtain the following table for the average number of steps: 12 11 1 9 359441 8 422327 7 446995 6 56188 5 6459 4 52785 3 5541 2 379818 1 342571 1 Note: A drawback of this system of equations method is that we do not obtain the average final height In Example 2, if we start at height j = 6, then about 66% of the time we will reach the top boundary first and about 34% of the time we hit the lower boundary first But these boundaries could be 1,, 1, 11, or 12 We do not have the probabilities of ending at each specific height, so we cannot find the average final height But in this case, we can use the Markov Chain Method with a large number of steps to find the average final height for a specific starting height j Mathematica Exercises (See systemnb file) 1 In a gambling game, you bet $3 at a time with a 4 : 3 payoff and a probability of p = 42 of winning (There are no ties) You want at least a 75% chance of reaching $3 (or more) before going bust (a) What is the minimum dollar amount with which you must start? (b) If you start with this amount, then what is the exact probability of attaining your goal, and what is the average number of bets you can make before attaining your goal or going bust? (c) In order to maximize the average number of bets you can make, with what dollar amount should you begin, and what would be the probability of attaining your goal when starting with this amount? 2 In a gambling game, let p = 1 and let the payoff be 8 : 1 (Again, no ties) You bet $1 at a time You wish to quit when you go ahead or go bust (ie, start with $1 and have a goal of $2 or more; or start with $3 and have a goal of $4 or more, etc) In general, you start with $1 k and have a top goal of $1 (k + 1) or more You want at least an 8% chance of attaining your goal What is the minimum dollar amount with which you must start? With this initial amount, what is the average number of bets that would be made?