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John Dagpunar

Bio: John Dagpunar is an academic researcher from University of Southampton. The author has contributed to research in topics: Random variate & Convolution random number generator. The author has an hindex of 15, co-authored 45 publications receiving 862 citations. Previous affiliations of John Dagpunar include University of Dundee & University of Edinburgh.

Papers
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Book
01 Sep 1988
TL;DR: In this paper, the FORTRAN 77 routines for generating variates from selected distributions and graphical methods for sampling from standardized gama distributions and the standardized normal distributions are presented. But they do not address the problem of sampling from distribution tails.
Abstract: Author's preface. Algorithmic conventions. Glossary. Simulation and random variate generation random number sequences general methods for generating random variates from unvariate distributions methods of generation from specific continuous distributions discrete distributions multivariate distributions miscellaneous topics, including the generation of order statistics, simulation of stochastic processes, sampling from distribution tails. Appendices: 1 - FORTRAN 77 routines for generating variates from selected distributions 2 - graphical methods for sampling from standardized gama distributions and the standardized normal distributions. References. Index.

151 citations

Journal ArticleDOI
TL;DR: In this paper, an algorithm for the generalised inverse Gaussian distribution using the ratio method is presented, which is shown to be fast over a wide range of distribution parameter values.
Abstract: Using the ‘ratio’ method an easily implemented algorithm is derived for the generalised inverse Gaussian distribution. Computer timings and efficiency calculations show that the procedure is fast over a wide range of distribution parameter values.

92 citations

MonographDOI
26 Jan 2007

85 citations

Journal ArticleDOI
TL;DR: In this paper, the optimal number of preventive maintenance actions to make over a warranty period under a free maintenance warranty is determined, where each preventive maintenance action reduces the system age by a fixed amount and all system failures are minimally repaired.

81 citations

Book
12 Mar 2007
TL;DR: Theoretical tests for random numbers are presented in this article, where the seed(s) in a random number generator are generated by linear congruential generators and a combination of generators.
Abstract: Preface. Glossary. 1 Introduction to simulation and Monte Carlo. 1.1 Evaluating a definite integral. 1.2 Monte Carlo is integral estimation. 1.3 An example. 1.4 A simulation using Maple. 1.5 Problems. 2 Uniform random numbers. 2.1 Linear congruential generators. 2.2 Theoretical tests for random numbers. 2.3 Shuffled generator. 2.4 Empirical tests. 2.5 Combinations of generators. 2.6 The seed(s) in a random number generator. 2.7 Problems. 3 General methods for generating random variates. 3.1 Inversion of the cumulative distribution function. 3.2 Envelope rejection. 3.3 Ratio of uniforms method. 3.4 Adaptive rejection sampling. 3.5 Problems. 4 Generation of variates from standard distributions. 4.1 Standard normal distribution. 4.2 Lognormal distribution. 4.3 Bivariate normal density. 4.4 Gamma distribution. 4.5 Beta distribution. 4.6 Chi-squared distribution. 4.7 Student's t distribution. 4.8 Generalized inverse Gaussian distribution. 4.9 Poisson distribution. 4.10 Binomial distribution. 4.11 Negative binomial distribution. 4.12 Problems. 5 Variance reduction. 5.1 Antithetic variates. 5.2 Importance sampling. 5.3 Stratified sampling. 5.4 Control variates. 5.5 Conditional Monte Carlo. 5.6 Problems. 6 Simulation and finance. 6.1 Brownian motion. 6.2 Asset price movements. 6.3 Pricing simple derivatives and options. 6.4 Asian options. 6.5 Basket options. 6.6 Stochastic volatility. 6.7 Problems. 7 Discrete event simulation. 7.1 Poisson process. 7.2 Time-dependent Poisson process. 7.3 Poisson processes in the plane. 7.4 Markov chains. 7.5 Regenerative analysis. 7.6 Simulating a G/G/1 queueing system using the three-phase method. 7.7 Simulating a hospital ward. 7.8 Problems. 8 Markov chain Monte Carlo. 8.1 Bayesian statistics. 8.2 Markov chains and the Metropolis-Hastings (MH) algorithm. 8.3 Reliability inference using an independence sampler. 8.4 Single component Metropolis-Hastings and Gibbs sampling. 8.5 Other aspects of Gibbs sampling. 8.6 Problems. 9 Solutions. 9.1 Solutions 1. 9.2 Solutions 2. 9.3 Solutions 3. 9.4 Solutions 4. 9.5 Solutions 5. 9.6 Solutions 6. 9.7 Solutions 7. 9.8 Solutions 8. Appendix 1: Solutions to problems in Chapter 1. Appendix 2: Random Number Generators. Appendix 3: Computations of acceptance probabilities. Appendix 4: Random variate generators (standard distributions). Appendix 5: Variance Reduction. Appendix 6: Simulation and Finance. Appendix 7: Discrete event simulation. Appendix 8: Markov chain Monte Carlo. References. Index.

58 citations


Cited by
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Journal ArticleDOI
TL;DR: A detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions, and a simple, intuitive derivation of this method is given along with guidance on implementation.
Abstract: We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions. A simple, intuitive derivation of this method is given along with guidance on implementation. Also discussed are two applications of the algorithm, one for implementing acceptance-rejection sampling when a blanketing function is not available and the other for implementing the algorithm with block-at-a-time scans. In the latter situation, many different algorithms, including the Gibbs sampler, are shown to be special cases of the Metropolis-Hastings algorithm. The methods are illustrated with examples.

3,886 citations

Journal ArticleDOI
Hongzhou Wang1
TL;DR: This survey summarizes, classifies, and compares various existing maintenance policies for both single-unit and multi-unit systems, with emphasis on single- unit systems.

1,507 citations

Book
01 Jan 2008
TL;DR: An introduction to survival and event history analysis can be found in this paper, where the authors present a nonparametric analysis of survival and history data using regression models and counting process models.
Abstract: An introduction to survival and event history analysis.- Stochastic processes in event history analysis.- Nonparametric analysis of survival and event history data.- Regression models.- Parametric counting process models.- Unobserved heterogeneity: The odd effects of frailty.- Multivariate frailty models.- Marginal and dynamic models for recurrent events and clustered survival data.- Causality.- First passage time models: Understanding the shape of the hazard rate.- Diffusion and L#x00E9 vy process models for dynamic frailty.

884 citations

Journal ArticleDOI
TL;DR: The maintenance of a deteriorating system is often imperfect: the system after maintenance will not as good as new, but younger as discussed by the authors, which indicates a significant breakthrough in reliability and maintenance theory.

595 citations

Journal ArticleDOI
TL;DR: A review of the literature that has appeared in the last ten years is presented in this paper, which highlights issues of interest to manufacturers in the context of managing new products from an overall business perspective.

398 citations