2 edition of Variance reduction techniques in simulation. found in the catalog.
Variance reduction techniques in simulation.
Jack P. C. Kleijnen
in [Tilburg, The Netherlands
Written in English
|LC Classifications||HA31.2 .K54|
|The Physical Object|
|Pagination||xii, 187 p.|
|Number of Pages||187|
|LC Control Number||72181505|
Variance reduction techniques are one way of doing this. These encompass a variety of methods that make an estimator more deterministic by incorporating additional information about a problem. The resulting estimators can have dramatically reduced standard errors while remain immune to the curse of dimensionality. This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, Author: Reuven Y. Rubinstein.
( views) Computer Simulation Techniques - The Definitive Introduction by Harry Perros - NC State University, The generation of pseudo-random numbers, the generation of stochastic variates, simulation designs, estimation techniques for analyzing endogenously created data, validation of a simulation model, variance reduction techniques. It is somewhat better with variance reduction techniques that often reaches errors proportional to \(1/K\). For a full overview of the convergence rate of the various methods, see for example the excellent book handbook of Monte Carlo methods by Kroese, Taimre and Botev. However as the number of dimensions grows, Monte Carlo convergence rate.
Sometimes can manipulate simulation to reduce the variance of the output at little or no additional cost — not just by running it some more Another way of looking at it — try to achieve a desired level of precision (e.g., confidence-interval smallness) with less simulating — Variance-reduction technique (VRT). We adapted these variance reduction techniques to the estimation methods of replication analysis and regenerative analysis by exploiting the asymptotic joint normality of certain standardized concomitant variables that are defined on each input process sampled during Cited by:
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However, before presenting these variance reduction techniques, let us illustrate the potential pitfalls, even in quite simple models, of using the raw simulation estimator. Example 9a Quality Control. Consider a process that produces items sequentially. Suppose that these items have measurable values attached to them and that when the process is “in control” these values.
where is the sample mean of the, is the population standard deviation, is the sample standard deviation of, and is the population mean.
The moment matching method can be extended to match covariance and higher moments as well. Stratified Sampling. Like many variance reduction techniques, stratified sampling seeks to make the inputs to simulation more regular than the.
The 5 th 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. This chapter develops methods for increasing the efficiency of Monte Carlo simulation by reducing the variance of simulation estimates.
These methods draw on two broad strategies for reducing variance: taking advantage of tractable features of a model to adjust or correct simulation outputs, and reducing the variability in simulation by: 1.
Variance Reduction. Careful design of a simulation experiment can almost always improve its effectiveness for a given cost, or reduce its cost for prescribed effectiveness. That is, the cost in computer time, for it is possible for the thought in the design process to outweigh the savings (as is the case for all the examples of this chapter.
Simulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.
This book, by two leading experts in the field, travels well-beyond the usual introduction to stochastic simulation and variance reduction to the heart of the adaptive tools required by the complex simulation and optimization problems of the next decade/5(7).
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.
Variance Reduction Techniques in Monte Carlo Methods. variance reduction is obtained when the change of measure has been. Rare-event simulation techniques: an introduction and.
Variance Reduction Techniques Introduction. In this chapter we discuss techniques for improving on the speed and eﬃciency of a simulation, usually called “variance reduction techniques”.
Much of the simulation literature concerns discrete event simulations (DES), simulations of systems that are assumed to change instantaneously in response. systems, validation techniques (e.g. goodness-of-fit tests; Kolmogorov-Smirnov tests), the standard MCMC-methods and various variance reduction techniques.
The latter chapter is the one that I found most enlightening including, for instance, a certainly rewarding section on importance sampling (weighted simulation). Throughout, a lot of5/5(3). Simulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic simulation and Monte Carlo techniques.
The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method. Simulation E ciency and an Introduction to Variance Reduction Methods 3 about the system. In this example, the system is Grassland’s climate, and what we know is E[R], the average daily rainfall.
We will now study control variates more formally, and in particular, we will determine by how much we should increase or decrease b Size: KB. The main variance reduction techniques discussed in this chapter are antithetic random variables, control variables, conditional Monte Carlo, stratification, latin hypercube sampling, importance sampling, and quasi Monte Carlo.
Each of the variance reduction methods is illustrated using the following estimation problem concerning a bridge network. In this chapter we investigate the problem of constructing variance reduced estimators for the expectation of functionals of solutions of SDEs that can speed up the simulation enormously.
We follow again closely Heath. As we will see, variance reduction is more of an art and can be applied in many by: 3. The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology.
Leading contributors cover such topics as discrete optimization via simulation. 4 8. Variance reduction Measuring e ciency Methods of variance reduction can sometimes bring enormous improvements compared to plain Monte Carlo.
It is not uncommon for the value ˙2 to be re-duced many thousand fold. It is also possible for a variance reduction technique to bring a very modest improvement, perhaps equivalent to reducing ˙2 by File Size: KB.
Variance Reduction Techniques. As we saw in Chap one of the many uses of Monte Carlo simulation by financial analysts is to place a value on financial derivatives. You now know that you can sharpen the point estimate of your derivative’s value by using the brute force method of increasing the number of trials run during a simulation.
The left side of equation (2) is the uniform distribution between 0 and 1 and f(y) is the distribution needed. In this way any distribution can be made with a uniform distribution. Monte Carlo results are obtained by simulating particle histories and assigning a score x i to each particle history.
The particle histories typically produce a range of score depending on the selected by: 8. TY - GEN. T1 - VARIANCE REDUCTION FOR SIMULATION PRACTITIONERS. AU - Nelson, Barry L. PY - /12/1. Y1 - /12/1. N2 - A comprehensive guide to applying three well-known variance reduction techniques (VRTs) is given, including point and interval estimators, software requirements, and guidelines for experiment by:.
This is caused by the need to incorporate simulation models into various outer algorithms for repetitive experiments, variance reduction techniques, and Author: Jaroslav Sklenar. Monte Carlo Simulation: Convergence and Variance reduction techniques for option pricing models In the following post we will further elaborate on these methods.
In particular we will be looking at the Antithetic Variable Technique and .7 Variance Reduction Techniques In a simulation study, we are interested in one or more performance measures for some stochastic model.
For example, we want to determine the long-run average waiting time,E.W/, of customers in a G=G=1 queue. To estimate E.W/ we can do a number of, say K, independent runs, the i-File Size: 28KB.