Justifying Linear Interpolation in Coin Toss Example

In summary, in class today, the conversation focused on an example involving a casino game and a gambler's goal to increase their fortune. The goal was represented by the event A and denoted by ψ(z). Through the use of the law of total probability, it was shown that ψ(z) is a linear function of z. This was justified by using a hypothesis and also by the characteristic equation for the recurrence relation. The conversation delved into the details of this justification, with one participant explaining that ψ(z) must increase by the same amount with every increment in z, making it a linear function.
  • #1
Mogarrr
120
6
Today in class, there was an example where I didn't understand certain justifications. The example goes something like this:

A casino runs a game of chance where you toss a coin and they pay $1 if you get heads , and you pay $1 if you get tails. The coin is a fair coin.

A gambler starts with fortune z. The goals is to increase the fortune to g≥z. Said gambler plays until they reach g or their fortune is $0.

Let A be the event of reaching the goal g starting from fortune z.
Let Pr(A) = ψ(z).

It's clear that ψ(0)=0 and ψ(g)=1, by the rules the gambler uses. We want a formula ψ(z) for 0<z<g. We use the law of total probability, conditioning on the first toss.
Let [itex]B_1[/itex]=first toss is heads
Let [itex]B_2[/itex]=first toss is tails, as this forms a partition of the sample space.

Pr(A)=[itex]Pr(B_1)\cdot Pr(A|B_1) + Pr(B_2)\cdot Pr(A|B_2) [/itex], so
ψ(z) = (1/2)ψ(z+1) + (1/2)ψ(z-1) = (1/2)(ψ(z+1)+ψ(z-1))

and this is where I have a big question mark over my head

ψ(z) is a linear function of z

[itex] \exists a,b: ψ(z)=az+b [/itex]
[itex] \psi (0)=0 \Rightarrow b=0 [/itex]
[itex] \psi (g) =1 \Rightarrow a = \frac 1{g} [/itex]
[itex] \Rightarrow \psi (z)= \frac {z}{g}, \forall z: 0 \leq z \leq g [/itex]

...So how is it justified that the recursive function is a linear function. The professor talked about a linear interpolation happening, but I just don't see how you can make that jump. I can follow all other lines of reasoning, so I just want to talk about this.

Anybody here know how that step is justified?
 
Physics news on Phys.org
  • #2
One way of thinking about that ##\psi## has the form of a linear equation is as a hypothesis. Only one equation will satisfy the recurrence relation given the two initial conditions. The general linear function has a solution in its coefficients, and so the hypothesis was correct, and the calculation yields the exact equation for ##\psi##.

But one can also deduce this without hypothesis of the form of ##\psi##. The characteristic equation for the recurrence relation has only one solution r = 1, and therefore we know the equation is linear, by the general theory of linear recurrence relations in one variable. Calculating the coefficients still remain however.
 
  • #3
disregardthat said:
But one can also deduce this without hypothesis of the form of ##\psi##. The characteristic equation for the recurrence relation has only one solution r = 1, and therefore we know the equation is linear, by the general theory of linear recurrence relations in one variable. Calculating the coefficients still remain however.

I don't really follow. I think the characteristic equation is a polynomial, right? What do you mean when you say it has only one solution, r=1? And the general theory of linear recurrence relations in one variable?
 
  • #4
Mogarrr said:
I don't really follow. I think the characteristic equation is a polynomial, right? What do you mean when you say it has only one solution, r=1? And the general theory of linear recurrence relations in one variable?

Read up some on recurrence relations. See for example here: http://en.wikipedia.org/wiki/Recurrence_relation#Solving
they do the general case for a recurrence relation in one variable, and what form it has when the characteristic roots are equal (in this case, equal to 1).

But this is of course much better explained in any book that covers this material.

To answer your questions, yes the characteristic equation is a polynomial equation (the characteristic polynomial = 0). It is a second-order polynomial equation, having two roots up to multiplicity.
 
  • Like
Likes 1 person
  • #5
Mogarrr said:
ψ(z) = (1/2)ψ(z+1) + (1/2)ψ(z-1) = (1/2)(ψ(z+1)+ψ(z-1))

The way I see it is if the above occurrence relation holds then

ψ(z+1) - ψ(z) = ψ(z) - ψ(z-1)

for any 0<z<g. That means ψ(z) must increase by the same amount with every increment in z (same difference going from z-1 to z as from z to z+1) or in other words, ψ(z) is linear.
 
  • Like
Likes 1 person
  • #6
rikblok said:
The way I see it is if the above occurrence relation holds then

ψ(z+1) - ψ(z) = ψ(z) - ψ(z-1)

for any 0<z<g. That means ψ(z) must increase by the same amount with every increment in z (same difference going from z-1 to z as from z to z+1) or in other words, ψ(z) is linear.

I see, so

[itex] \psi (z) = \frac 12 (\psi (z+1) + \psi (z-1)) \Rightarrow 2\cdot \psi (z) = \psi (z+1) + \psi (z-1) [/itex]
[itex] \Rightarrow \psi (z) - \psi (z-1) = \psi (z+1) - \psi(z) [/itex]

And as you say, ψ(z) increases by the same amount with every increment.

Very nice and easy to understand point.
 

What is linear interpolation?

Linear interpolation is a method used to estimate values between two known data points. It involves drawing a straight line between the two points and using the line to estimate values for points in between.

How is linear interpolation used in the coin toss example?

In the coin toss example, linear interpolation is used to estimate the number of heads or tails that will occur in a certain number of coin tosses. It is based on the assumption that the probability of getting heads or tails is constant and can be represented by a straight line.

Why is linear interpolation a valid method for estimating in the coin toss example?

Linear interpolation is a valid method for estimating in the coin toss example because it is based on the principle of the law of large numbers, which states that the more times an experiment is repeated, the closer the results will be to the expected outcome. In this case, as the number of coin tosses increases, the estimated number of heads or tails will approach the true probability.

What are the limitations of using linear interpolation in the coin toss example?

One limitation of using linear interpolation in the coin toss example is that it assumes a constant probability of getting heads or tails. In reality, the probability may change over time due to external factors. Additionally, linear interpolation may not be accurate if the sample size is too small or if there are outliers in the data.

Are there other methods for estimating in the coin toss example?

Yes, there are other methods for estimating in the coin toss example, such as using the binomial distribution or the central limit theorem. These methods take into account the changing probability and can provide more accurate estimates, but they may also require more complex calculations.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
28
Views
5K
  • Programming and Computer Science
Replies
10
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
946
  • Precalculus Mathematics Homework Help
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
16
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
0
Views
1K
Back
Top