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algorithms for generating non-uniform pseudorandom variates as a library of C functions designed and implemented by the ARVAG (Automatic Random VAriate Generation) project group in Vienna, and released under the GNU Public License (GPL). It is especially designed for situations where: - a non-standard distribution or a truncated distribution is needed; - experiments with different types of distributions are made; - random variates for variance reduction techniques are used; or - fast generators of predictable quality are necessary. UNU.RAN provides generators that are superior in many aspects to those found in quite a number of other libraries; however, due to its more sophisticated programming interface, it might not be as easy to use. It uses an object-oriented interface in which distributions and generators are treated as independent objects, so that different methods for generating non-uniform random variates may be chosen according to various criteria, such as speed, quality, and variance reduction. It is flexible enough to permit sampling from non-standard distributions, such as distributions that arise in a model and can only be computed in complicated subroutines. WWW: http://statmath.wu-wien.ac.at/unuran/ PR: ports/128883 Submitted by: bf <bf2006a at yahoo.com>
24 lines
1.3 KiB
Plaintext
24 lines
1.3 KiB
Plaintext
UNU.RAN (Universal Non-Uniform RAndom Number generator) is a collection of
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algorithms for generating non-uniform pseudorandom variates as a library of C
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functions designed and implemented by the ARVAG (Automatic Random VAriate
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Generation) project group in Vienna, and released under the GNU Public License
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(GPL). It is especially designed for situations where:
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- a non-standard distribution or a truncated distribution is needed;
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- experiments with different types of distributions are made;
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- random variates for variance reduction techniques are used; or
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- fast generators of predictable quality are necessary.
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UNU.RAN provides generators that are superior in many aspects to those found in
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quite a number of other libraries; however, due to its more sophisticated
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programming interface, it might not be as easy to use.
|
|
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It uses an object-oriented interface in which distributions and generators are
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treated as independent objects, so that different methods for generating
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non-uniform random variates may be chosen according to various criteria, such
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as speed, quality, and variance reduction. It is flexible enough to permit
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sampling from non-standard distributions, such as distributions that arise in
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a model and can only be computed in complicated subroutines.
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|
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WWW: http://statmath.wu-wien.ac.at/unuran/
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