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NonUniform Random Variate Generation
, 1986
"... Abstract. This is a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various ..."
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Cited by 623 (21 self)
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Abstract. This is a survey of the main methods in nonuniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
Random number generation
"... Random numbers are the nuts and bolts of simulation. Typically, all the randomness required by the model is simulated by a random number generator whose output is assumed to be a sequence of independent and identically distributed (IID) U(0, 1) random variables (i.e., continuous random variables dis ..."
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Cited by 136 (30 self)
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Random numbers are the nuts and bolts of simulation. Typically, all the randomness required by the model is simulated by a random number generator whose output is assumed to be a sequence of independent and identically distributed (IID) U(0, 1) random variables (i.e., continuous random variables distributed uniformly over the interval
NIST Net: A Linuxbased Network Emulation Tool
 Computer Communication Review
, 2003
"... Testing of network protocols and distributed applications has become increasingly complex, as the diversity of networks and underlying technologies increase, and the adaptive behavior of applications becomes more sophisticated. In this paper, we present NIST Net, a tool to facilitate testing and exp ..."
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Cited by 85 (0 self)
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Testing of network protocols and distributed applications has become increasingly complex, as the diversity of networks and underlying technologies increase, and the adaptive behavior of applications becomes more sophisticated. In this paper, we present NIST Net, a tool to facilitate testing and experimentation with network code through emulation. NIST Net enables experimenters to model and effect arbitrary performance dynamics (packet delay, jitter, bandwidth limitations, congestion, packet loss and duplication) on live IP packets passing through a commodity Linuxbased PC router. We describe the emulation capabilities of NIST Net; examine its architecture; and discuss some of the implementation challenges encountered in building such a tool to operate at very high network data rates while imposing minimal processing overhead. Calibration results are provided to quantify the fidelity and performance of NIST Net over a wide range of offered loads (up to 1 Gbps), and a diverse set of emulated performance dynamics. 1
Automatic Sampling with the RatioofUniforms Method
"... Applying the ratioofuniforms method for generating random variates results in very efficient, fast and easy to implement algorithms. However parameters for every particular type of density must be precalculated analytically. In this paper we show, that the ratioofuniforms method is also useful f ..."
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Cited by 19 (8 self)
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Applying the ratioofuniforms method for generating random variates results in very efficient, fast and easy to implement algorithms. However parameters for every particular type of density must be precalculated analytically. In this paper we show, that the ratioofuniforms method is also useful for the design of a blackbox algorithm suitable for a large class of distributions, including all with logconcave densities. Using polygonal envelopes and squeezes results in an algorithm that is extremely fast. In opposition to any other ratioofuniforms algorithm the expected number of uniform random numbers is less than two. Furthermore we show that this method is in some sense equivalent to transformed density rejection.
Continuous Random Variate Generation by Fast Numerical Inversion
, 2002
"... The inversion method for generating... In this paper we demonstrate that with Hermite interpolation of the inverse CDF we can obtain very small error bounds close to machine precision. Using our adaptive interval splitting method this accuracy is reached with moderately sized tables that allow for a ..."
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Cited by 13 (1 self)
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The inversion method for generating... In this paper we demonstrate that with Hermite interpolation of the inverse CDF we can obtain very small error bounds close to machine precision. Using our adaptive interval splitting method this accuracy is reached with moderately sized tables that allow for a fast and simple generation procedure.
Variants of Transformed Density Rejection and Correlation Induction
 and QuasiMonte Carlo Methods 2000
, 2001
"... In this paper we present some variants of transformed density rejection (TDR) that provide more exibility (including the possibility to halve the expected number of uniform random numbers) at the expense of slightly higher memory requirements. Using a synchronized rst stream of uniform variates and ..."
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Cited by 4 (1 self)
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In this paper we present some variants of transformed density rejection (TDR) that provide more exibility (including the possibility to halve the expected number of uniform random numbers) at the expense of slightly higher memory requirements. Using a synchronized rst stream of uniform variates and a second auxiliary stream (as suggested by Schmeiser and Kachitvichyanukul (1990)) TDR is well suited for correlation induction. Thus high positive and negative correlation between two streams of random variates with same or dierent distributions can be induced.
Universal Algorithms as an Alternative for Generating NonUniform Continuous Random Variates
 IN G. I
, 2000
"... This paper presents an overview of the most powerful universal methods. These are based on acceptance/rejection techniques where hat and squeezes are constructed automatically. Although originally motivated to sample from nonstandard distributions these methods have advantages that make them attra ..."
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Cited by 4 (2 self)
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This paper presents an overview of the most powerful universal methods. These are based on acceptance/rejection techniques where hat and squeezes are constructed automatically. Although originally motivated to sample from nonstandard distributions these methods have advantages that make them attractive even for sampling from standard distributions and thus are an alternative to special generators tailored for particular distributions. Most important are: the marginal generation time is fast and does not depend on the distribution. They can be used for variance reduction techniques, and they produce random numbers of predictable quality. These algorithms are implemented in a library, called UNURAN, which is available by anonymous ftp.
II. Statistical Computing II.1 Basic Computational Algorithms
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Random Variate Generation for
, 1997
"... this article deals with the fundamental problem for orthomonotone densities defined on the first quadrant (which is defined by the inequalities x i $ 0, 1 # i # d). We assume that the mode is at the origin and that f is zero off the first quadrant. Examples include the following (C is a normalizatio ..."
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this article deals with the fundamental problem for orthomonotone densities defined on the first quadrant (which is defined by the inequalities x i $ 0, 1 # i # d). We assume that the mode is at the origin and that f is zero off the first quadrant. Examples include the following (C is a normalization constant that may vary from equation to equation): (A) f# x 1 ,...,x d # 5 f i # x i #, x i $ 0, 1 # i # d, ACM Transactions on Modeling and Computer Simulation, Vol. 7, No. 4, October 1997
An Automatic Code Generator for Nonuniform Random Variate Generation
, 2001
"... There exists a vast literature on nonuniform random variate generators. Most of these generators are especially designed for a particular distribution. However in pratice only a few of these are available to practioners. Moreover for problems as (e.g.) sampling from the truncated normal distribution ..."
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There exists a vast literature on nonuniform random variate generators. Most of these generators are especially designed for a particular distribution. However in pratice only a few of these are available to practioners. Moreover for problems as (e.g.) sampling from the truncated normal distribution or sampling from fairly uncommon distributions there are often no algorithms available. In the last decade so called universal methods have been developed for these cases. The resulting algorithms are fast and have properties that make them attractive even for standard distributions.