libranlip 1.0-5 source package in Ubuntu
Changelog
libranlip (1.0-5) unstable; urgency=medium * QA upload * Convert to source format 3.0 (Closes: #1007491) * Update debhelper to compat 7 [ Helmut Grohne ] * Cross build should not produce build architecture binaries (Closes: #903538) -- Bastian Germann <email address hidden> Thu, 10 Aug 2023 01:59:00 +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 | Published | Component | Section | |
---|---|---|---|---|
Noble | release | universe | math | |
Mantic | release | universe | math |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
libranlip_1.0-5.dsc | 1.6 KiB | a18778c8ccfe98cf6d8ce54b32fbbc5f003dc61c251092b69d6054e33b76f2e9 |
libranlip_1.0.orig.tar.gz | 465.9 KiB | 885ad15711a6eddc2af4ded3a7bc4a3ca864e3b4ba2952f3e0c988961a05222a |
libranlip_1.0-5.debian.tar.xz | 4.5 KiB | 6391a14b6711a18b442b6dcb458a7e988244dff86484f654ac8088448063e8ea |
Available diffs
- diff from 1.0-4.4 to 1.0-5 (2.6 KiB)
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.