Re: [Boost-users] [Boost.Random] Discrete distribution behavior

Subject: Re: [Boost-users] [Boost.Random] Discrete distribution behavior
From: sguazt (marco.guazzone_at_[hidden])
Date: 2012-06-05 03:51:25

On Tue, Jun 5, 2012 at 7:57 AM, Steven Watanabe <watanabesj_at_[hidden]> wrote:
> AMDG
>
Thanks Steven!

[cut]

> The algorithm generates a table
> that looks like this:
>
> std::vector<std::pair<double, int> > table = {
>  { 0.3, 1 },
>  { 0.5, 2 },
>  { 1.0, 0 }
> };
>
> Then the generation algorithm is
>
> double x = uniform_01<>(engine);
> int result = uniform_int<>(0, 2)(engine);
> if (x < table[result].first) return result;
> else return table[result].second;
>

Sorry, I didn't have enough background knowledge on this topic.

My error was to relate the U(0,1) x number to a CDF value.
Anyway I performed a Monte-Carlo simulation and the results (the
empirical PDF) match with the original PDF.

Since I still don't understand how event weights are preserved and I
don't find a way to get the Walker's paper
   http://dx.doi.org/10.1145/355744.355749
could you point me to some reference or URL so that I can get
acquainted with Walker's method?

Thank you so much!

Best,

-- Marco