Mean and Variance of first hitting (entrance, passage) time

Click For Summary

Homework Help Overview

The discussion revolves around calculating the expected value and variance of the first hitting time in a random walk scenario, where the probabilities of stepping up or down are defined. The original poster presents the problem of determining E[T_k] and Var[T_k] for a given k, using specific probability equations related to the random walk.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants explore the use of probability equations and theorems related to first hitting times. Questions arise regarding the interpretation of certain expressions and the applicability of Stirling's approximation. There is also mention of consulting literature for deeper insights into the problem.

Discussion Status

Participants are actively engaging with the problem, raising questions about specific terms and suggesting references for further reading. Some guidance has been offered regarding the use of generating functions and the structure of the problem, indicating a productive direction in the discussion.

Contextual Notes

There are references to assumptions about the probabilities in the random walk, and the discussion includes considerations of infinite-state Markov chains and the need for specific conditions to derive finite moments. The original poster's approach and the responses indicate a complex interplay of mathematical concepts that may require additional context or information.

jimmy7430
Messages
2
Reaction score
0
Let Pr(X_{i} = +1) =\frac{2}{3} = 1 - Pr(X_{i} = -1), and S_{n} = \sum{X_{i}}, For each k≥1, define T_{k}\ =\ min \left\{n≥1: S_{n} = k \right\}. Calculate E[T_k], and Var[T_k].

2. Homework Equations

E[T_k] = \sum_{n=1}^{n=∞}{n Pr(T_k = n)}, and E[T_k]^2 = \sum_{n=1}^{n=∞}{n^2 Pr(T_k = n)} will be used.

The Attempt at a Solution



k is given as an known constant. A theorem says Pr(T_k = n ) = Pr(S_{1} S_{2} ... S_{n} ≠ 0, S_{n} = k) = \frac{|b|}{n}Pr(S_n = k), and
Pr(S_n = k) = \left( \stackrel{n}{\frac{1}{2}(n+k)} \right) p^{\frac{1}{2}(n+k)}q^{\frac{1}{2}(n-k)}

IMHO, problem should be asolved. However, It doesn't seem to have a nice expression. My guess will be use Sterling approximation, or Mathematica. Any comments are welcome.
 
Physics news on Phys.org
jimmy7430 said:
Let Pr(X_{i} = +1) =\frac{2}{3} = 1 - Pr(X_{i} = -1), and S_{n} = \sum{X_{i}}, For each k≥1, define T_{k}\ =\ min \left\{n≥1: S_{n} = k \right\}. Calculate E[T_k], and Var[T_k].

2. Homework Equations

E[T_k] = \sum_{n=1}^{n=∞}{n Pr(T_k = n)}, and E[T_k]^2 = \sum_{n=1}^{n=∞}{n^2 Pr(T_k = n)} will be used.

The Attempt at a Solution



k is given as an known constant. A theorem says Pr(T_k = n ) = Pr(S_{1} S_{2} ... S_{n} ≠ 0, S_{n} = k) = \frac{|b|}{n}Pr(S_n = k), and
Pr(S_n = k) = \left( \stackrel{n}{\frac{1}{2}(n+k)} \right) p^{\frac{1}{2}(n+k)}q^{\frac{1}{2}(n-k)}

IMHO, problem should be asolved. However, It doesn't seem to have a nice expression. My guess will be use Sterling approximation, or Mathematica. Any comments are welcome.

In your expression for P(T_k = n) (an expression that I don't believe), what is |b|? What is the meaning of
\left( \stackrel{n}{\frac{1}{2}(n+k)} \right)?

Anyway, using Stirling's (not Sterling's) approximation would not help you to get an *exact* expression, although it might help you to determination an approximate answer. Rather than going to Mathematica (or Maple), my first inclination would be to consult a good book, such as W. Feller, "An Introduction to Probability Theory and Its Applcications", Vol. I (Wiley). It has whole chapters on this material.

Alternatively, you can recognize that to get E(T_k) you need the expected first passage-time in an infinite-state Makov chain, and you can try to apply the usual equations for expected first-passage times to get an expression. (This will result from the solution of infinitely many coupled linear equations in infinitely many unknows, but the special structure makes it tractable.)

RGV
 
Ray Vickson said:
In your expression for P(T_k = n) (an expression that I don't believe), what is |b|? What is the meaning of
\left( \stackrel{n}{\frac{1}{2}(n+k)} \right)?

This is n choose \frac{1}{2}(n+k)

Appreciate your comments.
 
jimmy7430 said:
This is n choose \frac{1}{2}(n+k)

Appreciate your comments.

In Chapter 14, page 351 of the Feller book I cited before, there is a simple formula for the generating function U_z(s) = \sum_{n=0}^{\infty} s^n P\{T_z = n\} of the absorption time Tz in an absorbing random walk on (0,∞), with absorption at 0 and starting from z > 0. The walk has probabilities p and q = 1-p of unit steps up and down, respectively. The problem is essentially the same as yours: you have the interval (-∞,k) with absorption at k and you start from 0, while Feller's case has the interval (0,∞) with absorption at 0 and starts from z>0. Feller's expression is
U_z(s) = \left[ \frac{1-\sqrt{1-4pqs^2}}{2ps}\right]^z,
and you can get the moments E(T_z^m) from this in the usual way:
E\,T_z = U_z ^{\prime}(1),\; E\, T_z^2 = U_z ^{\prime \prime} (1) + U_z ^{\prime}(1) . For Feller's case you need to assume p < q in order to have finite mean and variance of absorption times, and when evaluating the derivatives using a computer package (I used Maple) that assumption must be passed to the system. Of course, you could do the derivatives manually, but this is one case where using a CAS really saves hours of work and a tree's worth of paper. The final results are amazingly simple.

In your case you want to go up instead of down, and you have p > q, so it is essentially equivalent.

RGV
 
Last edited:

Similar threads

Replies
9
Views
2K
  • · Replies 17 ·
Replies
17
Views
3K
  • · Replies 7 ·
Replies
7
Views
3K
  • · Replies 2 ·
Replies
2
Views
1K
  • · Replies 7 ·
Replies
7
Views
2K
Replies
2
Views
1K
  • · Replies 3 ·
Replies
3
Views
2K
Replies
32
Views
3K
  • · Replies 12 ·
Replies
12
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K