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Non-uniform random numbers
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1	2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) INDEPENDENT DOUBLY ADAPTIVE REJECTION METROPOLIS SAMPLING Luca Martino? , Jesse Read† , David Luengo‡ ? Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2014-08-22 09:13:11 Statistics Monte Carlo methods Computational statistics Markov chain Monte Carlo Statistical theory Non-uniform random numbers Rejection sampling MetropolisHastings algorithm Stochastic simulation Gibbs sampling
2	1 Improved Adaptive Rejection Metropolis Sampling Algorithms Luca Martino† , Jesse Read† , David Luengo‡ † Add to Reading List Source URL: arxiv.org Language: English - Date: 2012-10-09 00:12:52 Monte Carlo methods Markov chain Monte Carlo Non-uniform random numbers Rejection sampling MetropolisHastings algorithm Limit of a function
3	Lab Exercise: The rejection method of generating samples GEOS 627: Inverse Problems and Parameter Estimation, Carl Tape Last compiled: February 5, 2015 Overview • The purpose of this exercise is to understand one parti Add to Reading List Source URL: www.giseis.alaska.edu Language: English - Date: 2015-02-05 15:25:16 Monte Carlo methods Non-uniform random numbers Linear algebra MATLAB Mathematical software Rejection sampling Limit of a function
4	2013 IEEE International Conference on Computer Vision Slice Sampling Particle Belief Propagation Oliver Müller, Michael Ying Yang, and Bodo Rosenhahn Institute for Information Processing (TNT), Leibniz University Hanno Add to Reading List Source URL: www.tnt.uni-hannover.de Language: English - Date: 2016-08-02 06:36:10 Monte Carlo methods Markov chain Monte Carlo Non-uniform random numbers Probability distributions Computational statistics MetropolisHastings algorithm Rejection sampling Gibbs sampling Normal distribution Slice sampling Uniform distribution
5	A Level-set Hit-and-run Sampler for Quasi-Concave Distributions Shane T. Jensen and Dean P. Foster Department of Statistics, The Wharton School, University of Pennsylvania Abstract Add to Reading List Source URL: www-stat.wharton.upenn.edu Language: English - Date: 2014-05-24 19:58:02 Monte Carlo methods Markov chain Monte Carlo Non-uniform random numbers Probability distributions Computational statistics Gibbs sampling Rejection sampling MetropolisHastings algorithm Slice sampling Bayesian network Quasiconvex function Normal distribution
6	Chapter 9 Random Numbers This chapter describes algorithms for the generation of pseudorandom numbers with both uniform and normal distributions. Add to Reading List Source URL: www.mathworks.com Language: English - Date: 2013-09-25 13:50:03 Probability and statistics Non-uniform random numbers Randomness Ziggurat algorithm Random number generation Pseudorandomness Normal distribution George Marsaglia RANDU Pseudorandom number generators Mathematics Applied mathematics
7	Package ‘Runuran’ February 19, 2015 Type Package Title R interface to the UNU.RAN random variate generators VersionDate Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2015-02-19 16:36:26 Randomness Probability theory Monte Carlo methods Inverse transform sampling Probability distribution Quantile function Normal distribution Random number generation Probability mass function Probability and statistics Statistics Non-uniform random numbers
8	PDF Document Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2015-02-19 16:36:26 Randomness Mathematical analysis Monte Carlo methods Normal distribution Inverse transform sampling Random number generation Quantile function Random variate Cumulative distribution function Statistics Probability and statistics Non-uniform random numbers
9	Total Variation Reconstruction From Quasi-Random Samples Colas Schretter1,2 , Ignace Loris3 , Ann Dooms1,2 and Peter Schelkens1,2 1 Vrije Universiteit Brussel, Dept. of Electronics and Informatics, Pleinlaan 2, B-1050 Br Add to Reading List Source URL: homepages.ulb.ac.be Language: English - Date: 2014-02-26 11:10:13 Randomness Non-uniform random numbers Monte Carlo methods Numerical analysis Sampling Low-discrepancy sequence Peak signal-to-noise ratio Pseudo-random number sampling Inverse transform sampling Mathematics Applied mathematics Probability and statistics
10	Total Variation Reconstruction From Quasi-Random Samples ABSTRACT Pseudo-random numbers are often used for generating incoherent uniformly distributed sample distributions. Add to Reading List Source URL: homepages.ulb.ac.be Language: English - Date: 2014-11-17 05:12:41 Non-uniform random numbers Sampling Probability and statistics Randomness Numerical analysis Pseudorandomness Random sample Low-discrepancy sequence Pseudo-random number sampling Mathematics Statistics Applied mathematics

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