Le ven. 21 févr. 2020 à 14:04, Alan Grayson <[email protected]> a écrit :
> > > On Friday, February 21, 2020 at 3:41:29 AM UTC-7, Bruce wrote: >> >> On Fri, Feb 21, 2020 at 9:30 PM Bruno Marchal <[email protected]> wrote: >> >>> On 21 Feb 2020, at 04:40, Bruce Kellett <[email protected]> wrote: >>> >>> From: Brent Meeker <[email protected]> >>> >>> Of course that's true. But the more relevant value is the fraction of >>> sequences with the proportion of 1s within some narrow range of 0.5. For >>> large N, the distribution is Gaussian with std deviation ~sqrt(N) so almost >>> equal numbers of 1s and 0s do predominate. >>> >>> >>> I was aware of that, but they only dominate in a narrow range when p = >>> 0.5. My thinking was that since the confidence interval around the >>> estimated probability shrinks as 1/sqrt(N) for large N, outside a small >>> range of small deviations from equal numbers of zeros and ones, the >>> confidence interval on the probability estimates would no longer capture p >>> = 0.5. Also, looking at numbers of zeros within +- a small number of N/2 >>> would give results for the asymptotic proportion similar to those for N/2 >>> zeros. Since my calculation systematically ignores factors of the order of >>> one, I doubt that including such bit strings with close to equal numbers of >>> zeros and ones would make any significant difference to the conclusion that >>> such strings do not dominate in the limit. In other words, I think my >>> conclusion that the majority of the 2^N observers would not estimate >>> probabilities close to 0.5 is secure. (Ignoring factors of order one in the >>> calculation!) >>> >>> >>> But that argument would work for coin tossing too. That eliminate >>> basically all probabilistic inference, it seems to me. A dwarf and a giant >>> would not accept the Gaussian distribution of height. >>> >> >> >> You still don't get it, do you? The argument applies to all possible bit >> strings of length N. You do not get that from coin tosses in a single >> world. It is only when you claim that all possible results exist in >> separate branching worlds that the problem arises. So it is a problem for >> your WM-duplication, and for Everett. But not for single world theories. >> Statistical inference is perfectly intact as it is used in this world. >> >> Bruce >> > > Bruce; is WM the same as MW, as in Many Worlds, and if not, what's the > distinction? TIA, AG > Washington / Moscow duplication. > -- > You received this message because you are subscribed to the Google Groups > "Everything List" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To view this discussion on the web visit > https://groups.google.com/d/msgid/everything-list/3dfcd8fe-021d-402e-88dc-f1fd2daabeea%40googlegroups.com > <https://groups.google.com/d/msgid/everything-list/3dfcd8fe-021d-402e-88dc-f1fd2daabeea%40googlegroups.com?utm_medium=email&utm_source=footer> > . > -- All those moments will be lost in time, like tears in rain. (Roy Batty/Rutger Hauer) -- You received this message because you are subscribed to the Google Groups "Everything List" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/everything-list/CAMW2kArk7xEb_8EX25XL%3DRwXbwEO5pRJ-KFuxrEQ3EpAWXsNhA%40mail.gmail.com.
