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libranlip package

libranlip 1.0-4.2 source package in Ubuntu

Changelog

libranlip (1.0-4.2) unstable; urgency=medium

  * Non-maintainer upload.
  * Use dh-autoreconf to ensure builds use current autofoo and build
    on new architectures (Closes: #759434 #535999 #758031)

 -- Wookey <email address hidden>  Thu, 21 Jan 2016 23:10:42 +0000

Upload details

Uploaded by:
Juan Esteban Monsalve Tobon
Uploaded to:
Sid
Original maintainer:
Juan Esteban Monsalve Tobon
Architectures:
any
Section:
math
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Bionic release universe math
Xenial release universe math

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File Size SHA-256 Checksum
libranlip_1.0-4.2.dsc 1.7 KiB a3cc46be0dc455acc2d726fa4f73ce93c6bbdf87a48dc67f30e168c573bbad4b
libranlip_1.0.orig.tar.gz 465.9 KiB 885ad15711a6eddc2af4ded3a7bc4a3ca864e3b4ba2952f3e0c988961a05222a
libranlip_1.0-4.2.diff.gz 3.6 KiB 2e8088a3be312be965ca101e016be0726868ac6acdbf9794ccec7fc88b93a246

No changes file available.

Binary packages built by this source

libranlip-dev: generates random variates with multivariate Lipschitz density

 RanLip generates random variates with an arbitrary multivariate
 Lipschitz density.
 .
 While generation of random numbers from a variety of distributions is
 implemented in many packages (like GSL library
 http://www.gnu.org/software/gsl/ and UNURAN library
 http://statistik.wu-wien.ac.at/unuran/), generation of random variate
 with an arbitrary distribution, especially in the multivariate case, is
 a very challenging task. RanLip is a method of generation of random
 variates with arbitrary Lipschitz-continuous densities, which works in
 the univariate and multivariate cases, if the dimension is not very
 large (say 3-10 variables).
 .
 Lipschitz condition implies that the rate of change of the function (in
 this case, probability density p(x)) is bounded:
 .
 |p(x)-p(y)|<M||x-y||.
 .
 From this condition, we can build an overestimate of the density, so
 called hat function h(x)>=p(x), using a number of values of p(x) at some
 points. The more values we use, the better is the hat function. The
 method of acceptance/rejection then works as follows: generatea random
 variate X with density h(x); generate an independent uniform on (0,1)
 random number Z; if p(X)<=Z h(X), then return X, otherwise repeat all
 the above steps.
 .
 RanLip constructs a piecewise constant hat function of the required
 density p(x) by subdividing the domain of p (an n-dimensional rectangle)
 into many smaller rectangles, and computes the upper bound on p(x)
 within each of these rectangles, and uses this upper bound as the value
 of the hat function.

libranlip1c2: generates random variates with multivariate Lipschitz density

 RanLip generates random variates with an arbitrary multivariate
 Lipschitz density.
 .
 While generation of random numbers from a variety of distributions is
 implemented in many packages (like GSL library
 http://www.gnu.org/software/gsl/ and UNURAN library
 http://statistik.wu-wien.ac.at/unuran/), generation of random variate
 with an arbitrary distribution, especially in the multivariate case, is
 a very challenging task. RanLip is a method of generation of random
 variates with arbitrary Lipschitz-continuous densities, which works in
 the univariate and multivariate cases, if the dimension is not very
 large (say 3-10 variables).
 .
 Lipschitz condition implies that the rate of change of the function (in
 this case, probability density p(x)) is bounded:
 .
 |p(x)-p(y)|<M||x-y||.
 .
 From this condition, we can build an overestimate of the density, so
 called hat function h(x)>=p(x), using a number of values of p(x) at some
 points. The more values we use, the better is the hat function. The
 method of acceptance/rejection then works as follows: generatea random
 variate X with density h(x); generate an independent uniform on (0,1)
 random number Z; if p(X)<=Z h(X), then return X, otherwise repeat all
 the above steps.
 .
 RanLip constructs a piecewise constant hat function of the required
 density p(x) by subdividing the domain of p (an n-dimensional rectangle)
 into many smaller rectangles, and computes the upper bound on p(x)
 within each of these rectangles, and uses this upper bound as the value
 of the hat function.