Looking for the logic behind this

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The discussion centers on calculating the probability of a specific ball landing in its corresponding bowl when balls numbered 1 to n are dropped randomly into bowls also numbered 1 to n. The key logic involves understanding that the total arrangements of n balls is n!, while fixing one ball (ball x in bowl x) reduces the arrangements of the remaining balls to (n-1)!, leading to the probability formula (n-1)!/n!. The probability remains 1/n under uniform distribution assumptions, where each bowl has an equal chance of receiving any ball. The conversation also explores the implications of varying the number of successes and failures in matching, suggesting that analyzing failures may provide clearer insights into probability outcomes. Overall, the discussion highlights the complexities of probability in constrained scenarios.
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We got a bunch of balls numbered 1 to n. And we got a bunch of bowls numbered also 1 to n.

What is the chance of ball x hitting bowl x after dropping each ball randomly in a bowl one by one?

The answer involves saying 1/n after going (n-1)!/n! and I wonder, what is the full logic behind it?

I think I can get the denominator as a collection of all the shuffling results, but what is the exact logic that leads to the numerator being (n-1)!?
 
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Hey cdux.

The key thing you have to specify is whether the probability changes after a ball is released (or a specific bowl is hit).

If the probabilities don't change at all with respect to the above attributes then probability is always the same and under a uniform distribution (all probabilities are likely) then the probability is 1/n for all bowls.

If the assumptions are different then you will get a different distribution (the above is the simplest case with the easiest assumptions).
 
The question didn't specify so they are probably equiprobable. Being from the beginning of a book, I guess it's certain.

PS. I'm mainly interested in the logic of (n-1)!/n! (that led to 1/n) rather than going directly to 1/n.
 
When placing the kth ball, you have to put it in one of the k remaining empty bowls. You can't drop it on the floor or put it in your pocket. That places a constraint on the placement. You have k-1 choices rather than k choices as to where to place that kth ball. If you don't put it into one of the first k-1 empty bowls you have no choice but to drop it into the last empty bowl.
 
cdux said:
We got a bunch of balls numbered 1 to n. And we got a bunch of bowls numbered also 1 to n.

What is the chance of ball x hitting bowl x after dropping each ball randomly in a bowl one by one?

The answer involves saying 1/n after going (n-1)!/n! and I wonder, what is the full logic behind it?

I think I can get the denominator as a collection of all the shuffling results, but what is the exact logic that leads to the numerator being (n-1)!?

Try this: if ball x falls in bowl x, how many ways can the other n-1 balls be placed? What fraction is that of the total number of ways of placing n balls?
 
OK I have a mental image of a base of containers shuffling around that nets a result of n! states. Then if you hold into place k ball into container k then you can shuffle the balls only in (n-1)! ways which produces our desired (n-1)!/n! result for classical probability N(A)/N(Ω).

But there may be a missing link in the logic here because I suspect I was biased by knowing the answer when reaching that interpretation.

Is there really a concrete way of looking at probability that involves 'holding the desired result into place' while all the rest outcomes 'shuffle' around it?

It sounds impressive for a movie but I wonder if it has holes. i.e. exceptions.



The logic of subtracting elements from the numerator group for more successes appears to be correct since the answer is also (n-l)!/n! for l successes.

Going to the extreme of n successes appears to make it easier to understand since that would net 0!/n! which is what one would expect for the chance of getting an n! combination right.

If you're going to have 2 failures in matching, it doubles the probability of the contraption, and 3 failures can shuffle around in 6 ways making the probability of that X 6 of the initial. So it might be another case of thinking of failures instead of successes might be more direct.



..And you don't want to see the answer for "at least l successes..".
 
I was reading documentation about the soundness and completeness of logic formal systems. Consider the following $$\vdash_S \phi$$ where ##S## is the proof-system making part the formal system and ##\phi## is a wff (well formed formula) of the formal language. Note the blank on left of the turnstile symbol ##\vdash_S##, as far as I can tell it actually represents the empty set. So what does it mean ? I guess it actually means ##\phi## is a theorem of the formal system, i.e. there is a...

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