One thing not considered so far is that birthdays are not evenly
distributed - see http://www.panix.com/~murphy/bday.html - and this affects
the probability.

On Fri, Jan 27, 2012 at 9:50 AM, Linda Alvord <lindaalv...@verizon.net>wrote:

> I'm glad that we have finally covered most of the conditions that could
> occur. I'll enjoy pondering your work.  I'm working on a "narrower"
> challenge for next time that will not take us so far afield.
>
> Linda
>
> -----Original Message-----
> From: programming-boun...@jsoftware.com
> [mailto:programming-boun...@jsoftware.com] On Behalf Of Jose Mario
> Quintana
> Sent: Thursday, January 26, 2012 11:08 PM
> To: Programming forum
> Subject: Re: [Jprogramming] Challenge 4 Bountiful Birthdays
>
> Regarding the original task, one can proceed (knowing the actual densities)
> via a shifted multinomial simulation,
>
>   multinomial=. +/\ o [ <: o ((0: , [) I. ]) ? o $&0 o ] NB. dyadic verb
>
>   7 (samples=. ((densities o [) (2+multinomial) ]) ("0)) 10 10 10 10 10 NB.
> day of the week samples
> 2 3 4 6 5 6 2 6 2 6
> 3 4 2 8 2 5 4 5 3 5
> 3 2 2 6 2 3 4 5 2 3
> 4 5 5 6 2 6 5 7 2 3
> 5 2 3 5 5 5 4 5 4 7
>
>   mean=. +/ % #
>
>   365 (samplesmeans=. (mean"1 o samples)) 10000000 NB. day of the year 10
> million sample mean
> 24.6141
>
>   10 (] , mean) o (365 &samplesmeans) o # 500 NB. the original task
> 24.264 23.782 24.334 24.516 25.016 24.704 25.05 24.514 23.93 25.25 24.536
>
>   (] , mean) o (365 &samplesmeans) o # f. NB. according to the "simple"
> rules?
> (] , +/ % #)@:(365&((+/ % #)"1@:((+/\^:_1@:(1 - */\@:(1 - ] %~ 1 + i.))@:[
> (2 + +/\@:[ <:@:((0: , [) I. ]) ?@:$&0@:]) ])"0)))@:#
>
> Regarding accuracy, among other things, it can be argued that the
> distribution could even depend whether the experiment is conducted in the
> northern or the southern hemisphere (see
> http://www.panix.com/~murphy/bday.html and
> http://answers.google.com/answers/threadview/id/280242.html).  Models,
> maps,
> and other representations are ultimately doomed to be inaccurate; the
> subject matter is not only too complex but also evolving; above all, of
> course,  my representation of "the world" that is my mind is affected as
> well :)
>
> ________________________________________
> From: programming-boun...@jsoftware.com [programming-boun...@jsoftware.com
> ]
> On Behalf Of Jose Mario Quintana [josemarioquint...@2bestsystems.com]
> Sent: Tuesday, January 24, 2012 2:23 PM
> To: Programming forum
> Subject: Re: [Jprogramming] Challenge 4 Bountiful Birthdays
>
> >  +/(2+i.1000) * p * q NB. expected value
> > 24.6166
>
> I found the same solution in a slightly different way,
>
>   ((2 + i.) +/ .* +/\^:_1@:((1 - */\)@:(1 - ] %~ 1 + i.))) 365
> 24.6166
>
> The outline follows:
>
> It is easier to start dealing with the day of the week birthday process
> first,
>
>   (outcomes=. 2 + i.) 7 NB. all other outcomes have zero densities; thus,
> they are irrelevant
> 2 3 4 5 6 7 8
>
>   o=. @:
>
>   (cp=. 1 - ] %~ 1 + i.) 7 NB. conditional probabilities the process will
> not stop at each outcome given that it did not stop at its predecessor
> 0.857143 0.714286 0.571429 0.428571 0.285714 0.142857 0
>
>   cdf=. 1 - */\ o cp NB. cumulative distribution function
>
>   load'plot'
>   plot (0 0 , cdf) 7 NB. ploting the (smoothed) cdf
>
>   densities=. +/\^:_1 o cdf NB. since cdf -: +/\ densities
>
>    (mean=. outcomes +/ .* densities) 7 NB. formula for discrete densities
> 4.01814
>
> This generalizes to the day of the year birthday process,
>
>   plot (0 0 , cdf) 365
>   mean 365
> 24.6166
>
>   mean f.
> (2 + i.) +/ .* +/\^:_1@:(1 - */\@:(1 - ] %~ 1 + i.))
>
>
>
> ________________________________________
> From: programming-boun...@jsoftware.com [programming-boun...@jsoftware.com
> ]
> On Behalf Of Mike Day [mike_liz....@tiscali.co.uk]
> Sent: Friday, January 20, 2012 7:54 PM
> To: Programming forum
> Subject: Re: [Jprogramming] Challenge 4 Bountiful Birthdays
>
> My "trial" function,  listed earlier (and below) was
> not quite correct,  as it failed to count the
> successful person.
>
> So it should be:
>
>    trialb =: ([: # (] (,`]@.e.~ ([: ? 365"_)))^:_)"0
>
> So we get, for example (but it's very slow!  My variant
> triala discussed with Linda is somewhat better):
>
>    (mean, stdev) mean trialb 5000 100 $ _1
> 24.6133 0.180788
>
> Linda thinks the mean should be somewhat lower,  and
> Brian thinks it's a lot lower.  However, the standard
> deviation suggests it's close to the true value.
>
> I think this is the way to find the true expected number
> of people.  We don't need Markov after all:
>
>    Probability that (n-1) arrivals all have different
> b/days:
>
>    q =: Prod (1 - i%Y),  0<: i <: n-2,  Y =~ 365
>
>    Probability that the nth arrival's b/day is one of
> those present, ie is one of n-1 distinct bdays:
>
>    p =: (n-1) % Y
>
> Expected value of number of arrivals for "success":
>
>    Sum (2+i) pi * qi, 0 <: i <: n-2
>
> In J:
>    5{. q =: */\(1 - (365 %~(i.))) 1000
> 1 0.99726 0.991796 0.983644 0.972864
>
>    5 {. p =: (365 %~>:@i. )1000
> 0.00273973 0.00547945 0.00821918 0.0109589 0.0136986
>
>    +/(2+i.1000) * p * q   NB. expected value
> 24.6166
>
> This is not the same as the median, where the
> probability q moves below 0.5,
>
>    21 22 { q
> 0.524305 0.492703
>
> As Roger observes, the index origin comes into play;
> we should add one as the first person is 1, not zero (!)
> and the median group size is therefore just below 23.
>
> This last is dealing with a slightly different problem:
> what is the probability that a certain sized group of
> people do (not) share a birthday?  So we shouldn't be
> surprised at the difference.
>
> Mike
>
> On 18/01/2012 3:17 PM, Mike Day wrote:
> > People seem to be tackling two different problems.
> >
> > Variations on the Birthday Problem as I remember them:
> >  (a) what is the probability that two (or more) people
> > share a birthday in a group of N people?
> >  (b) what should N be for the probability to be (say) 0.5 ?
> > The somewhat counter-intuitive answers are dealt with in
> > Roger's Wiki Essay,  among many treatments, and also
> > Pablo's message, below.  The essential point is to
> > consider the probability that there are no matches.
> >
> > However, Linda's single trial as stated is a random
> > process with a stopping condition:
> >  take one person at a time until the new person shares a
> > birthday with those already present. The result is the
> > number of people including the new arrival.
> >
> > I expect you need a Markov Process approach to get the
> > exact expected value for the stopping number. Not proved!
> >
> > Here's a stab at the required simulation, avoiding @ and @:
> > though using [:
> >
> > NB. I use _1 as seed, so need to decrement the count
> >
> >    trial =: (_1 + [: # (] (,`]@.e.~ ([: ? 365"_)))^:_)"0
> >
> >    trial 10#_1  NB. eg conduct 10 trials
> > 27 19 29 2 24 42 30 9 34 33
> >
> >    mean =: +/%#    NB. ok for vectors or columns of matrix
> >
> >    ([:(;~mean) mean) TRIALS =: trial 500 10 $ _1
> >
>
> +-------+-------------------------------------------------------------------
> +
> >
> > |23.5882|22.696 23.676 23.894 24.044 23.874 23.56 24.258 23.416 22.81
> > 23.654|
> >
>
> +-------+-------------------------------------------------------------------
> +
> >
> >
> > These means are indeed close to N in problems
> > (a) & (b) where the probability is ~0.50,  namely
> > 21 for 0.475695 and 22 for 0.507297,  but not the
> > same.
> >
> > I used 365 rather than Pablo's 365.25 .  The simulation
> > could be done for 365.25,  using the integer 1461 (say).
> > The stopping condition would be a bit more complicated.
> >
> > The deviation of trials is quite large:
> >    SS  =:  [: *: (-"1 mean)  NB. squared deviations from mean
> >    stdev=: [: %: [: mean SS  NB. Observed Standard deviation
> > NB. not necessarily recommended for real, large sets of data
> >
> >    (mean,:stdev) TRIALS
> >  22.696  23.676  23.894  24.044 23.874   23.56  24.258 23.416   22.81
> > 23.654
> > 11.9378 12.6587 12.6917 12.5288 12.281 11.9741 12.1957 11.442 12.0969
> > 12.8718
> >
> > NB. standard deviation of the means:
> >
> >    (mean, stdev) mean TRIALS
> > 23.5882 0.477041
> >
> > Mike
> >
> >
> >
> >
>
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-- 
Devon McCormick, CFA
^me^ at acm.
org is my
preferred e-mail
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