Probability of a probability of a. .

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Discussion Overview

The discussion centers around the concept of "probability of a probability" within the frameworks of physics and mathematics. Participants explore the implications of conditional probabilities and the foundational axioms of probability theory, as well as examples illustrating these concepts.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • Some participants question whether a probability of a probability is permissible within the established axioms of probability theory, referencing Kolmogorov's framework.
  • One participant suggests that if an event has a probability [mu] of a probability [nu], it may imply a combined probability of [mu][nu], though this is not universally accepted.
  • Another participant relates the discussion to conditional probabilities, providing a formula that connects the probabilities of events A and B.
  • Examples are provided, such as the probability of getting three heads in four coin tosses and the interpretation of probabilities in terms of confidence levels in statistical contexts.
  • There is mention of the Gaussian curve as representing an infinite succession of probabilities, contrasting it with constant statistics.
  • Bayes' Theorem is referenced as a relevant concept for calculating conditional probabilities, with a suggestion to seek further information on it.

Areas of Agreement / Disagreement

Participants express differing views on the validity and interpretation of a probability of a probability, with some aligning on the relevance of conditional probabilities while others explore alternative interpretations. The discussion remains unresolved regarding the foundational aspects of this concept.

Contextual Notes

Some assumptions about the definitions and applications of probability are not fully articulated, and the implications of different interpretations of probability are not resolved.

Loren Booda
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Does physics or mathematics allow for a probability of a probability?
 
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As a mathematical theory, modern probability is based on a set of axioms formulated by Kolmogoroff in the 1930's. To make a probability of probability theory, one can see if the axioms make sense in this case.
 
mathman,

For instance, if an event has a probability [mu] of probability [nu] of occurring, then can you say in general that the event has a probability [mu][nu] of occurring?
 
What you are talking about sounds a lot like conditional probabilities.

For events A and B, if:

P(A) = μ
P(B | A) = ν (that's probability that B occurs, given that A occurs)
P(A | B) = 1 (B can only occur if A occurs)

Then we can apply the formula

P(A|B) P(B) = P(B|A) P(A)
to get

P(B) = μ ν
 
Sure:

I throw a coin four times, the probailty that I get three heads is 0.25, but you can also say: The probability that the probailty after the second throw is 0.0625 is 0.5.
 
And has Hurkyl says, my example is a conditonal probability.
 
Thanks much for your explanations, folks. I will try to recondition my thinking accordingly.

Could you recommend a simple online source for conditional probability, Hurkyl? The notation slips me.
 
In the U.S. the typical example of a conditional probability is someone making the second free throw:

Jeff Hornachek (Don't remember spelling) had a 90% free throw rate, so on a double free throw, he had a 81% (or 90% of 90%) chance of making his second shot.

An alternative example would be from statistics or zero knowledge proofs where probability is used as an expression of confidence. Be wary that this type of double probability is something different than the conditional probalitity described above.

For example, there is a 90% probability that that loaded die has a 70% chance of rolling a 6.

Or from polling: There is a 95% probability (expressing confidence in the poll) that each voter has a 45% probability of voting for Arnie.
 
Sorry, I don't know of any resources in particular... I'd think about any introduction to probability would talk about it though.
 
  • #10
'sOK, NateTG gave some excellent examples. Practical interpretations of multiple probabilities tend to elicit different physical variables for each expectation, though. My first free throw might anticipate more rebound action than the second.

A Gaussian curve might be described as an infinite succession of probabilities, whereas a constant statistic could not. Endless deviatives of the Gaussian attest to the potential underlying infinite series of probabilities.
 
  • #11

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