for the [@http://en.wikipedia.org/wiki/arcsine_distribution arcsine distribution]
defined on the interval \[['x_min, x_max]\] is given by:
-[figspace] [figspace] f(x; x_min, x_max) = 1 /([pi][sdot][sqrt]((x - x_min)[sdot](x_max - x_min))
+[expression f(x; x_min, x_max) = 1 /([pi][sdot][sqrt]((x - x_min)[sdot](x_max - x_min))]
For example, __WolframAlpha arcsine distribution, from input of
The Cumulative Distribution Function CDF is defined as
-[figspace] [figspace] F(x) = 2[sdot]arcsin([sqrt]((x-x_min)/(x_max - x))) / [pi]
+[expression F(x) = 2[sdot]arcsin([sqrt]((x-x_min)/(x_max - x))) / [pi]]
[graph arcsine_cdf]
The random variate ['x] is constrained to ['x_min] and ['x_max], (for our 'standard' distribution, 0 and 1),
and is usually some fraction. For any other ['x_min] and ['x_max] a fraction can be obtained from ['x] using
-[sixemspace] fraction = (x - x_min) / (x_max - x_min)
+[expression fraction = (x - x_min) / (x_max - x_min)]
The simplest example is tossing heads and tails with a fair coin and modelling the risk of losing, or winning.
Walkers (molecules, drunks...) moving left or right of a centre line are another common example.
The results were tested against a few accurate spot values computed by __WolframAlpha, for example:
N[PDF[arcsinedistribution[0, 1], 0.5], 50]
- 0.63661977236758134307553505349005744813783858296183
+ 0.63661977236758134307553505349005744813783858296183
[h4 Implementation]
-In the following table ['a] and ['b] are the parameters ['x_min][space] and ['x_max],
+In the following table ['a] and ['b] are the parameters ['x_min] and ['x_max],
['x] is the random variable, ['p] is the probability and its complement ['q = 1-p].
[table
and produced the resulting expression
- x = -a sin^2((pi p)/2)+a+b sin^2((pi p)/2)
+[expression x = -a sin^2((pi p)/2)+a+b sin^2((pi p)/2)]
Thanks to Wolfram for providing this facility.