Well ... I have SpamCop on my incoming e-mail, and it snagged every one of
the beasts ... *And* reported them to whatever authorities SpamCop has
available to handle spam reports. Those few ISPs that listen to SpamCop
reports will chastise the spammers for their aggression.
SpamCop costs $30US a
I haven't gotten around to assembling the toolset required to build R on
Windows, since most of what I do is smallish interactive problems. However,
another possibility would be to load CygWin/XFree86 on your laptop (which
I've done), then download Atlas 3.5.7 from SourceForge (which I've done),
To be more precise, the decimal number 0.1 does not have an exact binary
equivalent. A long time ago, there was a book called, IIRC, Pascal with
Style or something of that ilk, which set out the warning Never compare
floating point numbers for equality.
--
M. Edward (Ed) Borasky
mailto:[EMAIL
Unfortunately, the data are *non-negative*, not strictly positive. Zero is a
valid and frequent inter-arrival time. It is, IIRC, the most likely value of
a (negative) exponential distribution.
--
M. Edward (Ed) Borasky
mailto:[EMAIL PROTECTED]
http://www.borasky-research.net
Suppose that
Thanks!! It does look like the easiest thing is direct ML; the code for a
normal mixture is in the book, so all I have to do is modify that for a sum
of a hyper-exponential, for which I have an approximate mean and CV, and a
normal, for which I have an approximate mean and SD.
I have two big
I have a collection of data which includes inter-arrival times of requests
to a server. What I've done so far with it is use sm.density to explore
the distribution, which found two large peaks. However, the peaks are made
up of Gaussians, and that's not really correct, because the inter-arrival
