2 edition of **Statistical considerations in computer simulation experiments** found in the catalog.

Statistical considerations in computer simulation experiments

George S. Fishman

- 179 Want to read
- 1 Currently reading

Published
**1967**
by Rand Corporation] in [Santa Monica, Calif
.

Written in English

- Digital computer simulation.,
- Operations research.

**Edition Notes**

Statement | by George S. Fishman. |

The Physical Object | |
---|---|

Pagination | 7 leaves ; |

ID Numbers | |

Open Library | OL16526088M |

model, designing a simulation experiment, and performing simulation analysis are: Step 1. Identify the problem. Step 2. Formulate the problem. Step 3. Collect and process real system data. Step 4. Formulate and develop a model. Step 5. Validate the model. Step 6. Document model for future use. Step 7. Select appropriate experimental design. Step 8. Each attendee will receive a copy of the book Simulation Modeling and Analysis (5th Edition, McGraw-Hill, ) by Dr. Averill M. Law as part of their registration fee – this book is widely considered to be the “bible” of simulation with more than , copies in print citations. Versions of this seminar have been presented.

Costs: Cost considerations should be given for each step in the simulation process, purchasing simulation software if not already available, and computer resources. Obviously if these costs exceed the potential savings in altering the current system, then simulation should not be pursued. Research and Teaching Interests. computer simulation of stochastic systems stochastic processes statistical learning Nelson studies the design and analysis of computer simulation experiments, particularly issues of statistical efficiency (such as variance-reduction techniques), multivariate output analysis (such as multiple-comparison procedures and simulation optimization), .

The 5th edition of this popular book is essential for management science students, MBAs and practitioners in this area. It shows how practical simulation models are built and used, and provides the theory needed to do this - whatever software is used. It covers the modelling methods, computing and statistical requirements for successful simulation. Statistical significance of effects estimates? (i.e., are they real?) Luxury in simulation-based experiments: Replicate the whole design n times Get n observations on each effect Sample mean, sample variance, confidence interval, etc., on expected effects Effect is “real” if .

You might also like

European Scrutiny Committee.

European Scrutiny Committee.

Spain and defence of the West

Spain and defence of the West

Systematics collections and the law

Systematics collections and the law

Factors influencing survival of tall fescue (Festuca arundinacea Schreb.) propagules

Factors influencing survival of tall fescue (Festuca arundinacea Schreb.) propagules

Test of the tenderfoot

Test of the tenderfoot

Awakening Dreams & Night Visions

Awakening Dreams & Night Visions

Fathers and sons

Fathers and sons

persuasion industry

persuasion industry

southern continents

southern continents

Bears little Michigan

Bears little Michigan

Dance in the primary school

Dance in the primary school

Lectures on British colonization and empire.

Lectures on British colonization and empire.

Old textiles

Old textiles

Get this from a library. Statistical considerations in computer simulation experiments. [George S Fishman; Rand Corporation.]. Such a simulation is the generation of stochastic processes by Monte Carlo methods.

While simulation languages such as SIMSCRIPT and GPSS have been developed and refined during the past decade, very little work has been done in applying statistical methodology to the analysis of computer simulations.

(See also RM, RM) 7 pp. Ref. Simulation experiments are, of course, designed with particular objectives in mind — although, to be sure, many simulation studies are multi-objective. Some typical goals of computer simulation in Author: Linda Weiser Friedman. A key strength of simulation studies is the ability to understand the behavior of statistical methods because some “truth” (usually some parameter/s of interest) is known from the process of generating the data.

This allows us to consider properties of methods, such as by: Since a computer simulation experiment is indeed an experiment, it follows that one should give careful attention to experimental design questions prior to conducting a simulation experiment.

Within the context of two simulation experiments with an inventory model, this paper discusses factorial, fractional factorial, rotatable, and response Cited by: Many computer simulations are statistical sampling experiments performed on a model of the system under investigation.

Other papers discuss some of the variables involved in the statistical design and analysis of simulation experiments such as variance reduction techniques, generation of random variates, and experimental layout. This book describes computer simulation concepts then provides basic details about using discrete-event computer simulation for decision making.

Input data collection and analysis, model construction, project mechanics, output analysis, verification, validation, reporting, logic transfer, and robust experimental design are all covered in detail.

A Guide to Monte Carlo Simulations in Statistical Physics David P. Landau, Kurt Binder Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics and statistical mechanics, this book provides an introduction to computer simulations.

Some simulation experiments have been conducted taking into account different topologies for 8, 16 and 32 processors and synthetic workload distributions The initial workload distribution W is assumed to be a random vector, each element (w=load[i]) of which is drawn independently from an identical uniform distribution in [0,].

Each data point obtained in the experiment is the average of interpreted with statistics like any experiment. Computer Simulation Uses Computer simulation can be an expensive, time consuming, and complicated problem solving technique. Therefore, certain circumstances warrant its use.

Situations well suited to its application include the following (Table ): General Situation Examples. The simulation hypothesis or simulation theory is the proposal that all of reality, including the Earth and the rest of the universe, could in fact be an artificial simulation, such as a computer versions rely on the development of a simulated reality, a proposed technology that would be able to convince its inhabitants that the simulation was "real".

Simulation Compare and contrast a computer simulation vs. a real-world phenomenon. (LO 2) See a demo of using a computer model to run experiments. (LO 3) Speculate as to why computer models can be valuable scientific tools. (LO 5) Learn that models are representations of reality.

Not all features of the real world are incorporated in to models. Design and Analysis of Computer Experiments is a rapidly growing technique in statistical experimental design.

This paper aims to discuss some practical issues when designing a computer simulation and/or experiments for manufacturing systems. A case study approach is reviewed and presented. The major purpose of this paper is to evaluate the practical use of statistical techniques in both the generalization or analysis of simulation results, and the design of simulation experiments.

Computational Statistical Experiments in Matlab This book is intended as an undergraduate textbook on introductory to intermediate level Computa-tional Statistics. The goal is to equip students with some of the most useful tools in Computational Statistics and the ability to use them e ectively.

This will be achieved by maintaining balance be. the possibility arises in computer-simulation experiments. The role of experiment design in statistical methodology.- This work concludes with brief considerations on the factorial. fraction of the myriad statistical analytic methods are covered in this book, but my rough guess is that these methods cover 60%% of what you will read in the literature and what is needed for analysis of your own experiments.

In other words, I am guessing that the ﬁrst 10% of all methods available are applicable to about 80% of analyses. This book describes methods for designing and analyzing experiments that are conducted using a computer code rather than a physical experiment.

It discusses how to select the values of the factors at which to run the code (the design of the computer experiment) in light of the research objectives of. A discussion of the statistical problems that arise in computer simulation experiments. Three problem areas inherent in all stochastic system simulation models are discussed: verification, which determines whether a model actually behaves as an experimenter assumes it does; validation, which tests whether the model reasonably approximates a real system; and problem analysis, which seeks to.

Introduction To Simulation Modeling And Metamodeling --Ch. The Simulation Model And Metamodel --Ch. The Metamodel In Perspective: Statistical Considerations In Simulation Experiments --Ch.

Metamodeling --Ch. Survey Of Current Research --Ch. Metamodeling: Some Additional Examples --Appendix: The Linear Regression Model.

Researchers develop simulation models that emulate real-world situations. While these simulation models are simpler than the real situation, they are still quite complex and time consuming to develop.

It is at this point that metamodeling can be used to help build a simulation study based on a.Statistical significance Confidence intervals Power and robustness Degrees of freedom Non-parametric analysis 4 Descriptive statistics Counts and specific values Measures of central tendency Measures of spread Measures of distribution shape Statistical indices This book offers a systematic treatment of computer simulation in drug development.

It not only deals with the principles and methods of Monte Carlo simulation, but also the applications in drug development, such as statistical trial monitoring, prescription drug marketing, and molecular docking.