[math-toolkit] algorithms for "selfmade" distributions
Hi there, I've models which return distributions as a collection of 2D values ( probability, $ value ). Does the math toolkit offer me algorithms that I could use which such distributions? I look for algorithms to convolve two distributions, resampling, etc. Thanks ahead, Christian
Christian Henning wrote:
Hi there, I've models which return distributions as a collection of 2D values ( probability, $ value ). Does the math toolkit offer me algorithms that I could use which such distributions? I look for algorithms to convolve two distributions, resampling, etc.
Not at present no. It might make a useful addition though if you'd like to submit it ;-) John.
Hi John, thanks for the clarification and invitation. Let me ask you. Can you do convolution for the distributions that are available from the math toolkit? Thanks, Christian On Sat, Jun 21, 2008 at 6:28 AM, John Maddock <jz.maddock@googlemail.com> wrote:
Christian Henning wrote:
Hi there, I've models which return distributions as a collection of 2D values ( probability, $ value ). Does the math toolkit offer me algorithms that I could use which such distributions? I look for algorithms to convolve two distributions, resampling, etc.
Not at present no.
It might make a useful addition though if you'd like to submit it ;-)
John. _______________________________________________ Boost-users mailing list Boost-users@lists.boost.org http://lists.boost.org/mailman/listinfo.cgi/boost-users
Hi John, thanks for the clarification and invitation. Let me ask you. Can you do convolution for the distributions that are available from the math toolkit?
Nope, again that would be a useful addition: am I correct in thinking that you require something more than just the weighted average of two distributions? John.
Yes, more than just the weighted mean. What would need to change inside a pdf object when convolving two distributions? As far as I understand, and please correct me if I'm wrong, the pdf's are lightweight in a sense that every y-value is being computed on the fly based on the x-value? Thanks, Christian On Tue, Jun 24, 2008 at 1:44 PM, John Maddock <john@johnmaddock.co.uk> wrote:
Hi John, thanks for the clarification and invitation. Let me ask you. Can you do convolution for the distributions that are available from the math toolkit?
Nope, again that would be a useful addition: am I correct in thinking that you require something more than just the weighted average of two distributions?
John.
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Yes, more than just the weighted mean. What would need to change inside a pdf object when convolving two distributions? As far as I understand, and please correct me if I'm wrong, the pdf's are lightweight in a sense that every y-value is being computed on the fly based on the x-value?
Yes, the result is computed numerically each time the pdf or cdf etc functions are called, however the interface is just: result_type pdf(distribution_object, random_variable); So as long as there is some mechanism for calculating the result from the convolved distributions then the interface should be able to cope? Do you have a reference/web page I can look at for what you want to calculate so I can see what's involved? John.
John, all documents I have are internal company documents. I wasn't able to find a good website for what I want. But we use Intel's IPP and it is providing the function ippsConv_64f which does the trick. The reason I would like to use the Math-Toolkit is that we have humongous preprocessing before finally calling that function. For instance: * one of the two distibution to be convolved is empty or has just one point * the two distribution need to have the same delta In the end we just have some pdf's that need to be convolved for further processing. Given the nature of the beast they can have all kinds of legal shapes. In going forward, I think having a data structure for holding "selfmade" pdf's would be a big plus. I don't mind working on it, as long as, I have some guidance. Regards, Christian On Wed, Jun 25, 2008 at 4:37 AM, John Maddock <john@johnmaddock.co.uk> wrote:
Yes, more than just the weighted mean. What would need to change inside a pdf object when convolving two distributions? As far as I understand, and please correct me if I'm wrong, the pdf's are lightweight in a sense that every y-value is being computed on the fly based on the x-value?
Yes, the result is computed numerically each time the pdf or cdf etc functions are called, however the interface is just:
result_type pdf(distribution_object, random_variable);
So as long as there is some mechanism for calculating the result from the convolved distributions then the interface should be able to cope?
Do you have a reference/web page I can look at for what you want to calculate so I can see what's involved?
John. _______________________________________________ Boost-users mailing list Boost-users@lists.boost.org http://lists.boost.org/mailman/listinfo.cgi/boost-users
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