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billschnieder said:z <= x + y
Note that this inequality applies to a single triangle. What if you could only measure one side at a time. Assume that for each measurement you set the label of the side your instrument should measure and it measured the length destroying the triangle in the process. So you performed a large number of measurements on different triangles. Measuring <z> for the first run, <x> for the next and <y> for the next.
Do you believe the inequality
<z> <= <x> + <y>
Is valid? In other words, you believe it is legitimate to use those averages in your inequality to verify its validity?
It depends. Do the values <x>, <y> and <z> represent experimental averages or expectation values? It it is the former than the answer is NO. If it is the latter then the answer is "it depends on the properties of stochastic process generating the triangles", with a further twist that one cannot not obtain exact expectation value from a finite run, no matter how long. One can only get estimation of true expectation value which will contain some errors that would have to be accounted for.
It is a different case from Bell's. Bell works with expectations rather than sums and has extra assumptions about the process.billschnieder said:Funny thing, your statement in bold says, exactly the same set of triangles must be used to calculate the terms. Isn't it disingenuous then for you to suggest something different in Bell's case?
The answer to that is NO.billschnieder said:In other words -- Do you agree that for Bell's inequality to be guaranteed to be obeyed, the same set of photons must be used to calculate all three expectation values used in the inequalities ? Please I need a Yes/No answer here.
The answer is YES. Being stationary basically tells you there ia s well defined expectation value for P(a,b) which does not change with time and so one can estimate it using different sets of data ans still get the same result.billschnieder said:It is your claim therefore that you do not need to use the same set of particles, because the process generating the particles is stationary?
I need a Yes/No answer here.
I quote myself:billschnieder said:If you agree, then I suppose you have proof that that it is stationary.
In Bell's paper these assumptions are encoded in the probability density ρ(λ) being function of λ and nothing else and A(a,λ) and B(b,λ) being fully deterministic. These extra assumptions about the stochastic process behind the data is what allows one to estimate expectations of each side of the triangle independently.
The answer is YES. Such a process would violate the assumptions of Bell's theorem therefore the result would not be applicable. However I do not believe you can provide such evidence.If I were to provide evidence that process producing the photons is not stationary, will you concede therefore that expectation values from such a process is not compatible with Bell's inequality?? I need a Yes/No answer here.
Look up the definition of stationary process and see for yourself its connection to ρ(λ). In case of Bell, this property is explicitly encoded in ρ(λ), and that is what makes factorization possible. In your case the assumption of stationary process is not encoded anywhere in the math. When the authors reach factorization step they discover that something is amiss here and go into handwawing mode with disastorous results.I disagree, it is the factorization mentioned in equation (5) of Sica's paper above is the crucial step which introduces the assumption of stationary. That step is equivalent to going from the universally valid arithmetic inequality:
<z> <= <x + y>
To the statistical inequality
<x> <= <x> + <y>
Which is only obeyed when the process generating the triangles is stationary
How come they use their own derivations instead of Bell's for that. Why don't they identify an error in the original derivation for a change?They are not trying to improve Bell. They are explaining why datasets from experiments/QM are not compatible with Bell's inequality.
Not the λ itself, no, but that's what not I said. To compute estinations for expectation values they compute means and std. deviations and that just doesn't quite work if probability densiity is not stationary. And that's what ρ(λ) assumption is about.Experimenters do not know or care about lambda.
Where does he says that? These are two different things. The correlation P(a,b) is stationary but the individual measurements A_{i}, B_{i} are non-commuting.Non-commuting measurements are not compatible with stationarity as Sica explains. Therefore you can not use expectation values from QM/Experiments as valid terms for Bell's inequality.
Curiosier and curiosier. Can you quote the exact words (or better yet, formulas) to that effect?If Bell introduces stationarity as a requirement without cause, that is is problem. If Bell fails to realize that the stationarity requirement is incompatible with QM to start with, then that is his fault.
This is just data massaging gone wrong. I see you didn't comment on my statement that [STRIKE]there is another elephant in this room[/STRIKE] equation (16) and therefore (21) are so obviously wrong, they violate both theory and practice and even basic symmetry.Sica essentially says if the process producing the data is stationary, it should be possible to sort the datasets such that the number and pattern of switching between +1 and -1 in b1 and b2 are identical in the first two runs, and after doing that, you could *factor* out the a1 list from run 1 and the c2 list from run 2, recombine them and create a counterfactual "a1 c2" run. Therefore you do not need to measure run 3 at all.
Bell says here is the relationship between thee correlation numbers. So if you want to check it experimentally you go and measure said three numbers, what's so difficult about that? Why would you calculate the third number using the wrong formula instead?
NO. Stationary assumption does not fail. Instead the author fails to incorporate it properly into his math.If however, it is not possible to sort the data from experiment runs 1 and 2 as outlined above, your stationarity assumption fails and the inequality is not applicable to the data. Do you agree?
DK