Relevant bibliographies by topics / Multivariate random variate generation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multivariate random variate generation'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Multivariate random variate generation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Multivariate random variate generation"

1

Devroye, Luc. "Random variate generation for multivariate unimodal densities." ACM Transactions on Modeling and Computer Simulation 7, no. 4 (1997): 447–77. http://dx.doi.org/10.1145/268403.268413.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Beliakov, Gleb. "Class library ranlip for multivariate nonuniform random variate generation." Computer Physics Communications 170, no. 1 (2005): 93–108. http://dx.doi.org/10.1016/j.cpc.2005.03.105.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Matolak, D. W., I. Sen, and W. Xiong. "Generation of multivariate Weibull random variates." IET Communications 2, no. 4 (2008): 523. http://dx.doi.org/10.1049/iet-com:20070133.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Baeumer, Boris, and Mihály Kovács. "Approximating Multivariate Tempered Stable Processes." Journal of Applied Probability 49, no. 01 (2012): 167–83. http://dx.doi.org/10.1017/s0021900200008925.

Full text
Abstract:
We give a simple method to approximate multidimensional exponentially tempered stable processes and show that the approximating process converges in the Skorokhod topology to the tempered process. The approximation is based on the generation of a random angle and a random variable with a lower-dimensional Lévy measure. We then show that if an arbitrarily small normal random variable is added, the marginal densities converge uniformly at an almost linear rate, providing a critical tool to assess the performance of existing particle tracking codes.
APA, Harvard, Vancouver, ISO, and other styles
5

Baeumer, Boris, and Mihály Kovács. "Approximating Multivariate Tempered Stable Processes." Journal of Applied Probability 49, no. 1 (2012): 167–83. http://dx.doi.org/10.1239/jap/1331216840.

Full text
Abstract:
We give a simple method to approximate multidimensional exponentially tempered stable processes and show that the approximating process converges in the Skorokhod topology to the tempered process. The approximation is based on the generation of a random angle and a random variable with a lower-dimensional Lévy measure. We then show that if an arbitrarily small normal random variable is added, the marginal densities converge uniformly at an almost linear rate, providing a critical tool to assess the performance of existing particle tracking codes.
APA, Harvard, Vancouver, ISO, and other styles
6

Biggins, J. D., and A. Rahimzadeh Sani. "Convergence results on multitype, multivariate branching random walks." Advances in Applied Probability 37, no. 03 (2005): 681–705. http://dx.doi.org/10.1017/s0001867800000422.

Full text
Abstract:
We consider a multi-type branching random walk on d-dimensional Euclidian space. The~uniform convergence, as n goes to infinity, of a scaled version of the Laplace transform of the point process given by the nth generation particles of each type is obtained. Similar results in the one-type case, where the transform gives a martingale, were obtained in Biggins (1992) and Barral (2001). This uniform convergence of transforms is then used to obtain limit results for numbers in the underlying point processes. Supporting results, which are of interest in their own right, are obtained on (i) ‘Perron
APA, Harvard, Vancouver, ISO, and other styles
7

Biggins, J. D., and A. Rahimzadeh Sani. "Convergence results on multitype, multivariate branching random walks." Advances in Applied Probability 37, no. 3 (2005): 681–705. http://dx.doi.org/10.1239/aap/1127483742.

Full text
Abstract:
We consider a multi-type branching random walk on d-dimensional Euclidian space. The~uniform convergence, as n goes to infinity, of a scaled version of the Laplace transform of the point process given by the nth generation particles of each type is obtained. Similar results in the one-type case, where the transform gives a martingale, were obtained in Biggins (1992) and Barral (2001). This uniform convergence of transforms is then used to obtain limit results for numbers in the underlying point processes. Supporting results, which are of interest in their own right, are obtained on (i) ‘Perron
APA, Harvard, Vancouver, ISO, and other styles
8

Kachitvichyanukul, Voratas, and Bruce W. Schmeiser. "Binomial random variate generation." Communications of the ACM 31, no. 2 (1988): 216–22. http://dx.doi.org/10.1145/42372.42381.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kemp, C. D., and Adrienne W. Kemp. "Poisson Random Variate Generation." Applied Statistics 40, no. 1 (1991): 143. http://dx.doi.org/10.2307/2347913.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Morgan, B. J. T., and L. Devroye. "Non-Uniform Random Variate Generation." Biometrics 44, no. 3 (1988): 919. http://dx.doi.org/10.2307/2531615.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Multivariate random variate generation"

1

Karawatzki, Roman, Josef Leydold, and Klaus Pötzelberger. "Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1400/1/document.pdf.

Full text
Abstract:
Generating samples from multivariate distributions efficiently is an important task in Monte Carlo integration and many other stochastic simulation problems. Markov chain Monte Carlo has been shown to be very efficient compared to "conventional methods", especially when many dimensions are involved. In this article we propose a Hit-and-Run sampler in combination with the Ratio-of-Uniforms method. We show that it is well suited for an algorithm to generate points from quite arbitrary distributions, which include all log-concave distributions. The algorithm works automatically in the sense that
APA, Harvard, Vancouver, ISO, and other styles
2

Karawatzki, Roman, and Josef Leydold. "Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/294/1/document.pdf.

Full text
Abstract:
Generating samples from multivariate distributions efficiently is an important task in Monte Carlo integration and many other stochastic simulation problems. Markov chain Monte Carlo has been shown to be very efficient compared to "conventional methods", especially when many dimensions are involved. In this article we propose a Hit-and-Run sampler in combination with the Ratio-of-Uniforms method. We show that it is well suited for an algorithm to generate points from quite arbitrary distributions, which include all log-concave distributions. The algorithm works automatically in the sense that
APA, Harvard, Vancouver, ISO, and other styles
3

Sabah, Mohammad. "Random Variate Generation Web Service." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/35001.

Full text
Abstract:
Simulation and statistical applications often mimic the behavior of a random phenomenon by way of generating random observations that form a known or empirical probability distribution with estimated parameter values. Generation of such random observations is called Random Variate Generation (RVG). The number of simulation and statistical applications provided on the World Wide Web (Web) is on the rise. To facilitate the development of simulation and statistical applications on the Web by way of reuse, there is a need for providing RVG as a Web service. This research involves the development o
APA, Harvard, Vancouver, ISO, and other styles
4

Hörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. "Automatic Random Variate Generation for Unbounded Densities." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/848/1/document.pdf.

Full text
Abstract:
A new automatic algorithm for sampling from monotone, unbounded densities is presented. The user has to provide a program to evaluate the density and its derivative and the location of the pole. Then the setup of the new algorithm constructs different hat functions for the pole region and for the tail region, respectively. For the pole region a new method is developed that uses a transformed density rejection hat function of the inverse density. As the order of the pole is calculated in the setup, conditions that guarantee the correctness of the constructed hat functions are provided. Numerica
APA, Harvard, Vancouver, ISO, and other styles
5

Tirler, Günter, and Josef Leydold. "Automatic Nonuniform Random Variate Generation in R." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/382/1/document.pdf.

Full text
Abstract:
Random variate genration is an important tool in statistical computing. Many programms for simulation or statistical computing (e.g. R) provide a collection of random variate generators for many standard distributions. However, as statistical modeling has become more sophisticated there is demand for larger classes of distributions. Adding generators for newly required distribution seems not to be the solution to this problem. Instead so called automatic (or black-box) methods have been developed in the last decade for sampling from fairly large classes of distributions with a single piece of
APA, Harvard, Vancouver, ISO, and other styles
6

Hörmann, Wolfgang, and Josef Leydold. "Automatic Random Variate Generation for Simulation Input." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2000. http://epub.wu.ac.at/534/1/document.pdf.

Full text
Abstract:
We develop and evaluate algorithms for generating random variates for simulation input. One group called automatic, or black-box algorithms can be used to sample from distributions with known density. They are based on the rejection principle. The hat function is generated automatically in a setup step using the idea of transformed density rejection. There the density is transformed into a concave function and the minimum of several tangents is used to construct the hat function. The resulting algorithms are not too complicated and are quite fast. The principle is also applicable to random vec
APA, Harvard, Vancouver, ISO, and other styles
7

Hörmann, Wolfgang, and Josef Leydold. "Continuous Random Variate Generation by Fast Numerical Inversion." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/664/1/document.pdf.

Full text
Abstract:
The inversion method for generating non-uniform random variates has some advantages compared to other generation methods, since it monotonically transforms uniform random numbers into non-uniform random variates. Hence it is the method of choice in the simulation literature. However, except for some simple cases where the inverse of the cumulative distribution function is a simple function we need numerical methods. Often inversion by ``brute force" is used, applying either very slow iterative methods or linear interpolation of the CDF and huge tables. But then the user has to accept unnecessa
APA, Harvard, Vancouver, ISO, and other styles
8

Leydold, Josef, Gerhard Derflinger, Günter Tirler, and Wolfgang Hörmann. "An Automatic Code Generator for Nonuniform Random Variate Generation." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2001. http://epub.wu.ac.at/364/1/document.pdf.

Full text
Abstract:
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. In this contri
APA, Harvard, Vancouver, ISO, and other styles
9

Young, David J. "The generation of correlated Rayleigh random variates by discrete Fourier transform and quality measures for random variate generation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22432.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Derflinger, Gerhard, Wolfgang Hörmann, and Josef Leydold. "Random Variate Generation by Numerical Inversion when only the Density Is Known." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/1112/1/document.pdf.

Full text
Abstract:
We present a numerical inversion method for generating random variates from continuous distributions when only the density function is given. The algorithm is based on polynomial interpolation of the inverse CDF and Gauss-Lobatto integration. The user can select the required precision which may be close to machine precision for smooth, bounded densities; the necessary tables have moderate size. Our computational experiments with the classical standard distributions (normal, beta, gamma, t-distributions) and with the noncentral chi-square, hyperbolic, generalized hyperbolic and stable distribut
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Multivariate random variate generation"

1

Non-uniform random variate generation. Springer-Verlag, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Devroye, Luc. Non-uniform random variate generation. Springer-Verlag, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Hörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. Automatic Nonuniform Random Variate Generation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05946-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Devroye, Luc. Non-Uniform Random Variate Generation. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4613-8643-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Principles of random variate generation. Clarendon Press, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

K, Nagar D., ed. Matrix variate distributions. Chapman & Hall, 2000.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Devroye, Luc. Non-Uniform Random Variate Generation. Springer, 2013.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Boudreau, Joseph F., and Eric S. Swanson. Monte Carlo methods. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0007.

Full text
Abstract:
Monte Carlo methods are those designed to obtain numerical answers with the use of random numbers . This chapter discusses random engines, which provide a pseudo-random pattern of bits, and their use in for sampling a variety of nonuniform distributions, for both continuous and discrete variables. A wide selection of uniform and nonuniform variate generators from the C++ standard library are reviewed, and common techniques for generating custom nonuniform variates are discussed. The chapter presents the uses of Monte Carlo to evaluate integrals, particularly multidimensional integrals, and the
APA, Harvard, Vancouver, ISO, and other styles
9

Hörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. Automatic Nonuniform Random Variate Generation. Springer, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Automatic Nonuniform Random Variate Generation (Statistics and Computing). Springer, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Multivariate random variate generation"

1

Hörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. "Multivariate Distributions." In Automatic Nonuniform Random Variate Generation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05946-3_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Devroye, Luc. "Multivariate Distributions." In Non-Uniform Random Variate Generation. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4613-8643-8_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Thomopoulos, Nick T. "Generating Multivariate Random Variates." In Essentials of Monte Carlo Simulation. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6022-0_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Devroye, Luc. "Random Sampling." In Non-Uniform Random Variate Generation. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4613-8643-8_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tempo, Roberto, Giuseppe Calafiore, and Fabrizio Dabbene. "Random Number and Variate Generation." In Randomized Algorithms for Analysis and Control of Uncertain Systems. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4610-0_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Devroye, Luc. "Random Combinatorial Objects." In Non-Uniform Random Variate Generation. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4613-8643-8_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Devroye, Luc. "Discrete Random Variates." In Non-Uniform Random Variate Generation. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4613-8643-8_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Devroye, Luc. "The Random Bit Model." In Non-Uniform Random Variate Generation. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4613-8643-8_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. "Combination of Generation and Modeling." In Automatic Nonuniform Random Variate Generation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05946-3_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. "General Principles in Random Variate Generation." In Automatic Nonuniform Random Variate Generation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05946-3_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Multivariate random variate generation"

1

Kuhl, Michael E. "History of random variate generation." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247791.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Devroye, Luc. "Sample-based non-uniform random variate generation." In the 18th conference. ACM Press, 1986. http://dx.doi.org/10.1145/318242.318443.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Cheng, Russell C. H. "Selecting input models and random variate generation." In the 25th conference. ACM Press, 1993. http://dx.doi.org/10.1145/256563.256577.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Cheng, Russell C. H. "Distribution fitting and random number and variate generation." In the 24th conference. ACM Press, 1992. http://dx.doi.org/10.1145/167293.167304.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Devroye, Luc. "Random variate generation in one line of code." In the 28th conference. ACM Press, 1996. http://dx.doi.org/10.1145/256562.256623.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Thomas, David B., and Wayne Luk. "An FPGA-specific algorithm for direct generation of multi-variate Gaussian random numbers." In 2010 21st IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP). IEEE, 2010. http://dx.doi.org/10.1109/asap.2010.5541005.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Li, Yuan, and Minxuan Zhang. "A FPGA Based Parallel Multivariate Gaussian Random Number Generation Framework." In 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2019. http://dx.doi.org/10.1109/icaica.2019.8873482.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Knox, Michael A. "Multivariable Monte Carlo Analysis Methods in Traffic Accident Reconstruction Using Python." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-62242.

Full text
Abstract:
The analysis involved in reconstructing a traffic crash often deals with multiple, codependent, stochastic variables such as departure angle measurements, coefficient of friction measurements, vehicle stiffness coefficients, and acceleration rates. The resulting analysis should appropriately lead to a range of possible vehicle speeds rather than a single speed output value. All too often, this speed range is reported as a continuous uniform distribution in which lower- and upper-bound values are just as probable as any value in between. In reality, vehicle speed ranges are more complex than ca
APA, Harvard, Vancouver, ISO, and other styles
9

Kotecha, J. H., and P. M. Djuric. "Gibbs sampling approach for generation of truncated multivariate Gaussian random variables." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.756335.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Thomas, David B., and Wayne Luk. "Using FPGA resources for direct generation of multivariate Gaussian random numbers." In 2009 International Conference on Field-Programmable Technology (FPT). IEEE, 2009. http://dx.doi.org/10.1109/fpt.2009.5377680.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography