libranlip 1.0-4.4 source package in Ubuntu
Changelog
libranlip (1.0-4.4) unstable; urgency=medium * Non-maintainer upload. [ Dhole ] * Makefile.in: Pass -n via GZIP_ENV to avoid embedding timestamps. (Closes: #788000) * debian/rules: Pass -n to gzip when compressing files. (Closes: #788000) * debian/rules: Use consistent date in packaged files. (Closes: #788000) [ Valerie R Young ] * debian/rules: Sort md5sums files. (Closes: #846975) [ Vagrant Cascadian ] * debian/rules: Use standard buildflags. (Closes: #1007137) -- Vagrant Cascadian <email address hidden> Thu, 22 Sep 2022 11:16:14 -0700
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 | Published | Component | Section | |
---|---|---|---|---|
Lunar | release | universe | math |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
libranlip_1.0-4.4.dsc | 1.3 KiB | 518b36f6fc928be030160214fea76e9c9e559ab22699b4b149146fd0afba119d |
libranlip_1.0.orig.tar.gz | 465.9 KiB | 885ad15711a6eddc2af4ded3a7bc4a3ca864e3b4ba2952f3e0c988961a05222a |
libranlip_1.0-4.4.diff.gz | 4.2 KiB | 6d38ac3442a05494a426be392bd304a588b2690bbdd2b0dd679f111b3e05fb04 |
Available diffs
- diff from 1.0-4.3 to 1.0-4.4 (1.2 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.