Calculating Probabilities with Spinner Outcomes: One Way or Another

In summary, the expected number of spins required until all three outcomes occur is equal to $1 + \frac{a(b+c)}{bc} + \frac{b(c+a)}{ca} + \frac{c(a+b)}{ab} - \frac a{b+c} - \frac b{c+a} - \frac c{a+b}.$ This can be simplified using the fact that $a+b+c=1.$
  • #1
M R
44
0
A spinner has three possible outcomes which occur with probabilities a, b and c where a+b+c=1.

What is the expected number of spins required until all three outcomes are seen?

There's an easy way and a harder way to do this. Guess which I did first.
 
Mathematics news on Phys.org
  • #2
M R said:
A spinner has three possible outcomes which occur with probabilities a, b and c where a+b+c=1.

What is the expected number of spins required until all three outcomes are seen?

There's an easy way and a harder way to do this. Guess which I did first.
This is a variation on the coupon collector's problem. In the case when all the probabilities are equal ($a=b=c=1/3$), the expected number of spins is $3\bigl(1 +\frac12 + \frac13) = \frac{11}2.$

In the general case, I certainly don't see an easy way to approach the problem, and I don't get an easy-looking formula for the answer.

Write $A$, $B$, $C$ for the outcomes with probabilities $a$, $b$, $c$ respectively. If the first spin gives an $A$, then the expected number of spins until a $B$ or $C$ occurs is $\dfrac1{b+c}$. The probability that this outcome is a $B$ is $\dfrac b{b+c}$, in which case the expected number of further spins until a $C$ turns up is $1/c.$ And the probability that a $C$ occurs before a $B$ is $\dfrac c{b+c}$, in which case the expected number of further spins until a $B$ turns up is $1/b.$ Therefore the total expected number of spins for all three outcomes to occur (given that the $A$ appears first) is $$1 + \frac1{b+c}\Bigl(\frac b{b+c}\,\frac1c + \frac c{b+c}\,\frac1b\Bigr) = 1 + \frac{b^2+c^2}{(b+c)^2bc} = \frac1{bc} - \frac2{(1-a)^2}$$ (in the last step, I have written the $b^2+c^2$ in the numerator as $(b+c)^2 - 2bc$, and in the denominator $b+c = 1-a$).

Multiply that by $a$, which is the probability of the $A$ occurring first, add two similar terms for the probabilities of $B$ or $C$ occurring first, and you get the answer for the expected number of spins as $$ 1 + \frac{a^2+b^2+c^2}{abc} - 2\biggl(\frac a{(1-a)^2} + \frac b{(1-b)^2} + \frac c{(1-c)^2}\biggl).$$

That looks messy, not the sort of thing that you could find easily? But it does reduce to $11/2$ when $a=b=c=1/3$, which makes me think that it should be correct.
 
  • #3
Hi Opalg

Maybe it wasn't easy but it was much easier than the other method which I will post if no one else does.

Your formula does give the right answer for a=b=c but not for other possibilities.

For comparison purposes:

a=1/2, b=1/3, c=1/6 should give 73/10

and

a=9/20, b=9/20, c=1/10 should give 353/33.
 
  • #4
M R said:
Your formula does give the right answer for a=b=c but not for other possibilities.

For comparison purposes:

a=1/2, b=1/3, c=1/6 should give 73/10

and

a=9/20, b=9/20, c=1/10 should give 353/33.
Stupid stupid mistake! My method was correct but I left out a $+$ sign, converting a sum into a product. The expression $$1 + \frac1{b+c}\Bigl(\frac b{b+c}\,\frac1c + \frac c{b+c}\,\frac1b\Bigr)$$
(for the expected number of spins for all three outcomes to occur, given that the $A$ appears first) should have been $$1 + \frac1{b+c} + \Bigl(\frac b{b+c}\,\frac1c + \frac c{b+c}\,\frac1b\Bigr) = 1 + \frac{bc + b^2+c^2}{(b+c)bc} = 1 + \frac{(b+c)^2 - bc}{(b+c)bc} = 1 + \frac{b+c}{bc} - \frac1{b+c}. $$ The answer for the total expected number of spins then comes out as $$ 1 + \frac{a(b+c)}{bc} + \frac{b(c+a)}{ca} + \frac{c(a+b)}{ab} - \frac a{b+c} - \frac b{c+a} - \frac c{a+b}.$$ That gives values agreeing with your results for a=1/2, b=1/3, c=1/6 and for a=9/20, b=9/20, c=1/10.
 
  • #5
The history of this problem (for me personally):

On another forum someone posted a 'hard probability question'. It was the a=b=9/20, c=1/10 case. Looking back at that forum I see that I managed to get an answer six days later. :eek:

Months later I saw a post on MHF which taught me a better approach so I went back and did it again, the easy way. :D Unfortunately I can't view MHF to find out who my teacher was.:(

I think it's essentially the same as Opalg's method but here's the 'easy' method as I did it:

Events A, B and C have probabilities a, b and c.

Let the expected waiting time until A occurs be [tex]E(W_A)[/tex],

and the expected waiting time until A and B occur be [tex]E(W_{AB})[/tex] and so on.

[tex]E(W_{AB})=c(1+E(W_{AB}))+a(1+E(W_B))+b(1+E(W_A))[/tex]

[tex]E(W_{AB})=c(1+E(W_{AB}))+a(1+1/b)+b(1+1/a)[/tex]

[tex]E(W_{AB})=1 +cE(W_{AB})+a/b+b/a[/tex]

[tex]E(W_{AB})=\frac{1+a/b+b/a}{1-c}[/tex]

Similarly

[tex]E(W_{AC})=\frac{1+a/c+c/a}{1-b}[/tex]

and

[tex]E(W_{BC})=\frac{1+b/c+c/b}{1-a}[/tex]

Then [tex]E(W_{ABC})=a(1+E(W_{BC}))+b(1+E(W_{AC}))+c(1+E(W_{AB}))[/tex]

and the method I used at first:

[sp]

By thinking about how the sequence ends I knew I wanted all the ways to get As and Bs ending with C etc.

ABC
BAC

AABC
ABAC
BAAC

ABBC
BABC
BBAC

etc.

This led me to the sum

[tex]\displaystyle E(W)=\sum_{n=2}^\infty (n+1) \sum_{r=1}^{n-1} \binom{n}{r}(a^rb^{n-r}c+a^rc^{n-r}b+b^rc^{n-r}a)[/tex]

[tex]\displaystyle =\sum_{n=2}^\infty (n+1) [ c((a+b)^n-a^n-b^n) +b((a+c)^n-a^n-c^n)+a((b+c)^n-b^n-c^n)][/tex]

This sum involves a number of geometric progressions and a number of sums of another type.

The other type is of the form [tex]\displaystyle S=\sum_{n=2}^\infty n x^n[/tex].

Multiplying by [tex]\displaystyle x [/tex] gives [tex]\displaystyle Sx=\sum_{n=2}^\infty n x^{n+1}[/tex] and so [tex]\displaystyle S-Sx = 2x^2+x^3+x^4+...[/tex]

[tex]\displaystyle S(1-x)=x^2 + \frac{x^2}{1-x}[/tex] and [tex]\displaystyle S=\frac{x^2}{1-x}+\frac{x^2}{(1-x)^2}[/tex]

Applying this formula, together with the formula for the sum of a GP we get

[tex]\displaystyle E(W)=[/tex]

[tex]\displaystyle (a+b)^2-\frac{ca^2}{1-a}-\frac{cb^2}{1-b}[/tex]

[tex]\displaystyle + (a+c)^2-\frac{ba^2}{1-a}-\frac{bc^2}{1-c}[/tex]

[tex]\displaystyle + (b+c)^2-\frac{ab^2}{1-b}-\frac{ac^2}{1-c}[/tex]

[tex]\displaystyle +(a+b)^2(1+1/c)-c\left(\frac{a^2}{1-a}+\frac{a^2}{(1-a)^2}+\frac{b^2}{1-b}+\frac{b^2}{(1-b)^2}\right)[/tex]

[tex]\displaystyle +(a+c)^2(1+1/b)-b\left(\frac{a^2}{1-a}+\frac{a^2}{(1-a)^2}+\frac{c^2}{1-c}+\frac{c^2}{(1-c)^2}\right)[/tex]

[tex]\displaystyle +(b+c)^2(1+1/a)-a\left(\frac{b^2}{1-b}+\frac{b^2}{(1-b)^2}+\frac{c^2}{1-c}+\frac{c^2}{(1-c)^2}\right)[/tex]

What a mess!

This could be simplified using [tex]\displaystyle a+b+c=1[/tex] but having checked it against the first method I'm happy that it's correct and I'm not interested in doing the simplification. :p
[/SPOILER]
 

FAQ: Calculating Probabilities with Spinner Outcomes: One Way or Another

What is the purpose of calculating probabilities with spinner outcomes?

The purpose of calculating probabilities with spinner outcomes is to determine the likelihood of a particular event occurring based on the possible outcomes of a spinner. This can be useful in various fields such as statistics, economics, and game theory.

How do you calculate the probability of a specific outcome with a spinner?

To calculate the probability of a specific outcome with a spinner, you need to divide the number of favorable outcomes by the total number of possible outcomes. For example, if a spinner has 4 equal sections and you want to know the probability of landing on a certain color, you would divide 1 (the number of favorable outcomes) by 4 (the total number of possible outcomes) to get a probability of 1/4 or 25%.

What is the difference between theoretical and experimental probability?

Theoretical probability is based on mathematical calculations and assumes that all outcomes are equally likely. On the other hand, experimental probability is based on actual data collected from repeated trials and may vary from the theoretical probability. As more trials are conducted, the experimental probability should approach the theoretical probability.

Can you use calculating probabilities with spinner outcomes to predict future events?

Calculating probabilities with spinner outcomes can give an idea of the likelihood of a certain event occurring, but it cannot guarantee that the event will happen in the future. The probability is based on previous outcomes and does not take into account any external factors that may affect the outcome.

Are there any limitations to using calculating probabilities with spinner outcomes?

One limitation of using calculating probabilities with spinner outcomes is that it assumes all outcomes are equally likely, which may not always be the case in real-world situations. Additionally, the accuracy of the calculated probabilities can be affected by the sample size and the randomness of the spinner. It is important to consider these limitations when interpreting the results of probability calculations.

Back
Top