There are many tests of normality that might be well suited. The Kolmogorov-Smirnov test ( http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test) for instance should be easy to compute in terms of the function erf().
On Mon, Dec 10, 2012 at 7:07 PM, Darren Cook <[email protected]> wrote: > > How much [effort] to determine whether there are multiple peaks? > > The Shapiro-Wilk test can give you a probability of how non-normal the > distribution is: > http://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test > > As an R example, here is some test data: > set.seed(7); > data <- c(rnorm(2000,0,40),rnorm(2500, 0, 20), rnorm(400, 40, 5)); > hist(data,breaks=200) > > and running shapiro.test(data) gives me: > W = 0.9939, p-value = 1.184e-13 > > The lower the p-value the more it thinks it is not a normal curve. The > extreme result is interesting, as the graph looks "roughly normal" to me. > > (The Wikipedia page lists alternative tests, which can be found in the R > nortest package apparently. I've no idea of CPU effort required for each > of them.) > > > Now the tough question: How can this information be used to improve move > selection? > > One approach, not at all sophisticated, is better time management: spend > less time on normal distributions, more time when the distribution is > messy. (But I wonder if more time will just make the two peaks stand > out more?) > > Darren > > -- > Darren Cook, Software Researcher/Developer > > http://dcook.org/work/ (About me and my work) > http://dcook.org/blogs.html (My blogs and articles) > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >
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