On Friday, February 21, 2020 at 6:10:26 AM UTC-7, Quentin Anciaux wrote: > > > > Le ven. 21 févr. 2020 à 14:04, Alan Grayson <[email protected] > <javascript:>> 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. >
I haven't been following that discussion in great detail. How does that come up, approximately? I apologize for my previous anger. AG -- 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/c024050a-3241-4d79-ac6f-96a167442ee8%40googlegroups.com.
