: A rejection algorithm -- called transformed density rejection -- that uses a new method for constructing simple hat functions for an unimodal, bounded density f is introduced. It is based on the idea to transform f with a suitable transformation T such that T (f(x)) is concave. f is then called T -concave and tangents of T (f(x)) in the mode and in a point on the left and right side are used to construct a hat function with table-mountain shape. It is possible to give conditions for the optimal choice of these points of contact. With T = \Gamma1= p x the method can be used to construct a universal algorithm that is applicable to a large class of unimodal distributions including the normal, beta, gamma and t-distribution. AMS Subject Classification: 65C10, 68C25. CR Categories and Subject Descriptors: G.3 [Probability and Statistics]: Random number generation General Terms: Algorithms Additional Key Words and Phrases: Rejection method, log-concave distributions, universal method 1....

In this paper we propose a new version of the Controlled Random Search (CRS) algorithm of Price [13, 14, 15]. The new algorithm has been tested on thirteen global optimization test problems. Numerical experiments indicate that the resulting algorithm performs considerably better than the earlier versions of the CRS algorithms. The algorithm, therefore, could offer a reasonable alternative to many currently available stochastic algorithms, especially for problems requiring `direct search' type methods. Also a classification of the CRS algorithms is made based on `global technique' -- `local technique' and the relative performance of classes is numerically explored. Keywords: Global optimization, fi-distribution, controlled random search 1 Introduction A global optimization algorithm aims at finding a global minimizer or its close approximation of a function f : S ae R n ! R. A point x is said to be a global minimizer of f if f = f(x ) f(x); 8x 2 S. We assume that the func...

Zusammenfassung Generating Beta Variates Via Patchwork Rejection. A new algorithm for sampling from beta(p; q) distributions with parameters p ? 1, q ? 1 is developed. It is based on a method by Minh [9] which improves acceptance--rejection sampling in the main part of the distributions. Additionally, transformed uniform deviates can often be accepted immediately, so that much fewer than two uniforms are needed for one beta variate, on the average. The remaining tests for acceptance are enhanced by 'squeezes'. Experiments covering a wide range of pairs (p; q) showed improvements in speed over competing algorithms in most cases.

C--RAND is a system of Turbo--C routines and functions intended for use on microcomputers. It contains up--to--date random number generators for more than thirty univariate distributions. For some important distributions the user has the choice between extremely fast but rather complicated methods and somewhat slower but also much simpler procedures. Menu driven demo programs allow to test and analyze the generators with regard to speed and quality of the output. 1.

for any errors or inaccuracies that may appear in this document or any software that may be provided in association with this document. This document and the software described in it are furnished under license and may only be used or copied in accordance with the terms of the license. No license, express or implied, by estoppel or otherwise, to any intellectual property

The information in this document is subject to change without notice and Intel Corporation assumes no responsibility or liability for any errors or inaccuracies that may appear in this document or any software that may be provided in association with this document. This document and the software described in it are furnished under license and may only be used or copied in accordance with the terms of the license. No license, express or implied, by estoppel or otherwise, to any intellectual property rights is granted by this document. The information in this document is provided in connection with Intel products and should not be construed as a commitment by Intel Corporation.

Abstract not found

This package implements random number generators from various standard distributions. It also provides an interface to the C package UNURAN. CONTENTS

Abstract. We consider the problem of the exact simulation of random variables Z that satisfy the distributional identity Z L = V Y + (1 − V)Z, where V ∈ [0, 1] and Y are independent, and L = denotes equality in distribution. Equivalently, Z is the limit of a Markov chain driven by that map. We give an algorithm that can be automated under the condition that we have a source capable of generating independent copies of Y, and that V has a density that can be evaluated in a black box format. The method uses a doubling trick for inducing coalescence in coupling from the past. Applications include exact samplers for many Dirichlet means, some two-parameter Poisson–Dirichlet means, and a host of other distributions related to occupation times of Bessel bridges that can be described by stochastic fixed point equations. Keywords and phrases. Random variate generation. Perpetuities. Coupling from the past. Random partitions. Stochastic recurrences. Stochastic fixed point equations. Distribution theory. Markov chain Monte Carlo. Simulation. Expected time analysis. Bessel bridge. Poisson-Dirichlet. Dirichlet means.

This package implements random number generators from various standard distributions. It also provides an interface to the C package UNURAN. CONTENTS