Ubuntu
libranlip package

libranlip 1.0-6 source package in Ubuntu

Changelog

libranlip (1.0-6) unstable; urgency=medium

  * QA upload.

  * Added d/gbp.conf to describe branch layout.
  * Updated vcs in d/control to Salsa.
  * Updated d/gbp.conf to enforce the use of pristine-tar.
  * Updated Standards-Version from 3.7.2 to 4.7.0.
  * Use wrap-and-sort -at for debian control files.
  * Trim trailing whitespace.
  * Bump debhelper dependency to >= 7, since that's what is used
    in debian/compat.
  * Bump debhelper from deprecated 7 to 10.
  * Set field Upstream-Name in debian/copyright.
  * Remove field Priority on binary packages libranlip-dev, libranlip1c2
    that duplicates source.
  * Adjusted d/rules to not strip binaries when DEB_BUILD_OPTIONS=nostrip
    is used (Closes: #437398).

 -- Petter Reinholdtsen <email address hidden>  Fri, 07 Jun 2024 08:10:53 +0200

Upload details

Uploaded by:
Debian QA Group
Uploaded to:
Sid
Original maintainer:
Debian QA Group
Architectures:
any
Section:
math
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
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Downloads

File Size SHA-256 Checksum
libranlip_1.0-6.dsc 1.9 KiB cec6f6420d702b6dc46ae105b57119efc6a5ab2536953317a1accd4e3eb6569c
libranlip_1.0.orig.tar.gz 465.9 KiB 885ad15711a6eddc2af4ded3a7bc4a3ca864e3b4ba2952f3e0c988961a05222a
libranlip_1.0-6.debian.tar.xz 4.9 KiB 03d5e6c9d2c7b475aaa0b19da371029059f3e49c207ecbaacd4c694af3e1b0eb

Available diffs

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.