Limited probabilities : a nonsense ?

In summary, the frequentist approach to probability is based on the limit of relative frequency as the number of trials approaches infinity. It is not applicable to non-repeatable experiments or single iterations. This approach is considered limited and is now based on the Kolmogorov axioms. The probabilities of QM are defined by these axioms, not by circular reasoning.
  • #1
jk22
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Suppose i have an experiment which can give result 0,4 but that the probability p(4)<=1/sqrt(2).

Does this make sense in a frequentist approach since if i do the real experiment once and got 4 then the probability (statistics a posteriori) for 4 is 1 which is a dumb counterexample.
 
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  • #2
In the frequentist approach, the probability is the limit of the relative frequency as the number of trials approach infinity, so it doesn't make sense in the frequentist approach to apply probability to non-repeatable experiments or single iterations. This is why the frequentist approach is considered by many to be of very limited applicability.
 
  • #3
The frequentest approach the way its sometimes presented in beginning texts at the high school level is incorrect - its circular. What is probability - the ratio of a large number of trials. Why does it work - the law of large numbers - and around it goes.

The correct basis is the Kolmogorov axioms:
http://en.wikipedia.org/wiki/Probability_axioms

From that you can prove the law of large numbers.

When you apply it you need to make a few reasonableness assumptions such as a very small probability can for all practical purposes be taken as zero. You then apply the law of large numbers to events and say there is conceptually a very large number of trials where the outcomes are in proportion to the probability. The issue is the law of large numbers converges almost assuredly meaning for a very large, but finite number of trials, there is a small probability it will not be in proportion. But that's where the reasonableness assumption comes in - the very small probability of this being the case is taken as zero.

If you want to pursue it further the QM forum is not the place to do it - there is a specific sub-forum devoted to discussing probability. That said any good book on probability such as the classic by Feller will explain it:
https://www.amazon.com/dp/0471257087/?tag=pfamazon01-20

Thanks
Bill
 
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  • #4
billschnieder said:
In the frequentist approach, the probability is the limit of the relative frequency as the number of trials approach infinity, so it doesn't make sense in the frequentist approach to apply probability to non-repeatable experiments or single iterations. This is why the frequentist approach is considered by many to be of very limited applicability.

That's why these days the frequentest approach is based on the Kolmogorov axioms. The probability exists regardless of what trials you do. To experimentally determine it you need to conduct a large number of trials, so large that for all practical purposes it will give the correct probability ie you have decided on a very small probability you will take as being zero.

Thanks
Bill
 
  • #5
This question was in fact linked to qm in the following sense : suppose you find the eigenvalues of bell chsh are 4 and 0 but that p(4)<=1/sqrt2 such that tsirelson bound is respected. Would this makes any sense ?
 
  • #6
jk22 said:
This question was in fact linked to qm in the following sense : suppose you find the eigenvalues of bell chsh are 4 and 0 but that p(4)<=1/sqrt2 such that tsirelson bound is respected. Would this makes any sense ?

Of course it would. The probabilities of QM are defined by the Kolmogerov axioms - not by circular frequent reasoning.

Thanks
Bill
 

FAQ: Limited probabilities : a nonsense ?

What are limited probabilities?

Limited probabilities refer to probabilities that are restricted or constrained in some way. This could mean that the probabilities only apply to a specific subset of events or outcomes, or that they are limited by certain assumptions or conditions.

How are limited probabilities different from regular probabilities?

Limited probabilities differ from regular probabilities in that they are not universally applicable. Regular probabilities can be used to describe any event or outcome, while limited probabilities are specific to certain situations or constraints.

What is the purpose of studying limited probabilities?

Studying limited probabilities can help us better understand and analyze complex systems or situations where regular probabilities may not be applicable. It allows us to take into account specific constraints or conditions that may affect the likelihood of certain outcomes.

Can limited probabilities be used in real-world applications?

Yes, limited probabilities can be used in various real-world applications such as risk assessment, decision making, and statistical analysis. They can help us make more accurate predictions and decisions in situations where regular probabilities may not be suitable.

Are there any limitations to using limited probabilities?

As with any type of probability, there are limitations to using limited probabilities. They may not always accurately reflect the true likelihood of an event or outcome, and their usefulness may be limited to specific situations or conditions. Additionally, the interpretation and application of limited probabilities may vary among different contexts and individuals.

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