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] > <javascript:>> wrote: > >> On 21 Feb 2020, at 04:40, Bruce Kellett <[email protected] >> <javascript:>> wrote: >> >> From: Brent Meeker <[email protected] <javascript:>> >> >> 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 -- 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.
