On 06/07/2018 1:18 PM, Berry, Charles wrote:
A liitle math goes along way. See below.
On Jul 5, 2018, at 10:35 PM, Marino David <davidmarino...@gmail.com> wrote:
Dear Bert,
I know it is a simple question. But for me, at current, I fail to implement
it. So, I ask for help here.
It is not homework.
Best,
David
2018-07-06 13:32 GMT+08:00 Bert Gunter <bgunter.4...@gmail.com>:
Is this homework?
(There is an informal no-homework policy on this list).
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Thu, Jul 5, 2018 at 10:23 PM, Marino David <davidmarino...@gmail.com>
wrote:
Dear All,
I would like to generate N random Bernoulli draws given a probability
function F(x)=1-exp(-2.5*x) in which x follows uniform distribution, say
x~U(0,2).
If each Bernoulli draw is based on its own draw of x, then
rbinom( N, 1, 0.8013476 )
is what you want.
It is left as an exercise for the reader to verify that the constant 0.8013476
is correct up to approximation error, and to prove that such a Bernoulli
mixture is also Bernoulli. Perhaps,
?integrate
will help.
But if the x's are shared you need to use runif, expm1, and (possibly) rep to
produce a vector to be used in place of the prob argument.
That may be correct from a mathematical perspective (I haven't checked),
but seems like a *really* bad idea from a programming perspective. It
would be much better to write
x <- runif(N, 0, 2)
rbinom(N, 1, 1 - exp(-2.5*x))
because it is so much more clearly related to the original problem
statement. Perhaps it would be a few microseconds slower, but that
would be saved many times over when any aspect of the problem statement
was modified.
Duncan Murdoch
HTH,
Chuck
Can some one leave me some code lines for implementing this?
Thanks in advance.
David
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