SimulationAcademic Press, 31 déc. 2012 - 328 pages The 5th edition of Ross's Simulation continues to introduce aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This latest edition features all-new material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis. Additionally, the 5th edition expands on Markov chain monte carlo methods, and offers unique information on the alias method for generating discrete random variables. By explaining how a computer can be used to generate random numbers and how to use these random numbers to generate the behavior of a stochastic model over time, Ross's Simulation, 5th edition presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model. - Additional material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis - Additional material and examples on Markov chain Monte Carlo methods - Unique material on the alias method for generating discrete random variables - Additional material on generating multivariate normal vectors |
Table des matières
| 5 | |
Random Numbers | 39 |
Generating Discrete Random Variables | 47 |
Variables | 60 |
Generating Continuous Random Variables | 69 |
The Multivariate Normal Distribution and Copulas | 97 |
The Discrete Event Simulation Approach | 111 |
Variance Reduction Techniques | 153 |
Additional Variance Reduction Techniques | 233 |
Statistical Validation Techniques | 247 |
Markov Chain Monte Carlo Methods | 271 |
| 303 | |
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Expressions et termes fréquents
algorithm antithetic variables approximately average Bernoulli CBSM compute conditional distribution conditional expectation consider control variable copula coupons Cov(X data values denote the number density function determine discrete random variable distribution function equal Equation Example exponential random variables exponential with rate follows function f gamma Gibbs sampler given Hence identically distributed importance sampling independent and identically independent random variables interval joint distribution large number Markov chain nonhomogeneous Poisson process nonnegative normal distribution normal random variable number of events obtain occurs outcomes p-value p₁ Poisson random variable preceding probability mass function process with rate random numbers random vector raw simulation estimator rejection method Reset result sequence server simulation runs simulation to estimate specified standard normal random STEP stratified sampling Suppose we want technique U₁ uniformly distributed Var(X variable with mean variable with parameters variance reduction X₁ Y₁