| Paketname | libranlip-dev |
| Beschreibung | generates random variates with multivariate Lipschitz density |
| Archiv/Repository | Offizielles Debian Archiv squeeze (main) |
| Version | 1.0-4.1 |
| Sektion | libdevel |
| Priorität | optional |
| Installierte Größe | 80 Byte |
| Hängt ab von | libc6 (>= 2.7-1), libgcc1, libranlip1c2 (= 1.0-4.1), libstdc++6 (>= 4.1.1-21), libtnt-dev |
| Empfohlene Pakete | |
| Paketbetreuer | Juan Esteban Monsalve Tobon |
| Quelle | libranlip |
| Paketgröße | 15228 Byte |
| Prüfsumme MD5 | 6f12783ae0f066bcdb3c73fe16b75c1f |
| Prüfsumme SHA1 | a996659f8208d947cc23b16657b77ca4c66cbf19 |
| Prüfsumme SHA256 | ce0735246d7efa894edb58031a3c05f407bba5c5e05b21d29dc7c940063feb1f |
| Link zum Herunterladen | libranlip-dev_1.0-4.1_i386.deb |
| Ausführliche Beschreibung | 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)|=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.
|