libranlip 1.0-4.1ubuntu1 source package in Ubuntu
Changelog
libranlip (1.0-4.1ubuntu1) trusty; urgency=low
* Use debhelper and its autoreconf helper to correctly build shared
libraries on newer arches, resolving FTBFS.
-- Daniel T Chen <email address hidden> Mon, 24 Mar 2014 16:21:01 -0400
Upload details
- Uploaded by:
- Daniel T Chen
- Uploaded to:
- Trusty
- Original maintainer:
- Ubuntu Developers
- Architectures:
- any
- Section:
- math
- Urgency:
- Low Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Trusty | release | universe | math |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| libranlip_1.0.orig.tar.gz | 465.9 KiB | 885ad15711a6eddc2af4ded3a7bc4a3ca864e3b4ba2952f3e0c988961a05222a |
| libranlip_1.0-4.1ubuntu1.diff.gz | 3.6 KiB | 329c8be43eb21b2da37b9911ad5482fcc911f63d6c3d6d2a84bde5db752eaac3 |
| libranlip_1.0-4.1ubuntu1.dsc | 1.5 KiB | ff587d2dcc6046feb8f955936ab76d6a87d6998fd186c55d9486076c4f1266a1 |
Available diffs
- diff from 1.0-4.1 to 1.0-4.1ubuntu1 (804 bytes)
Binary packages built by this source
- libranlip-dev: No summary available for libranlip-dev in ubuntu wily.
No description available for libranlip-dev in ubuntu wily.
- 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.
