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1 edition of Comparing combat models using analytical surrogates found in the catalog.

Comparing combat models using analytical surrogates

Green, John R.

Comparing combat models using analytical surrogates

by Green, John R.

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  • 3 Currently reading

Published by Naval Postgraduate School, Available from the National Technical Information Service in Monterey, Calif, Springfield, Va .
Written in English


About the Edition

The widespread availability of inexpensive high-speed computers has led to the development of complex, detailed technical models of combat. These high resolution computer simulations and wargames are touted by their proponents as low-cost alternatives to extensive, high-cost field training exercises for the training of combat leaders. The validity of these simulations as models of combat, and thus as useful training tools is unproven. Direct comparison of simulations with field training exercises is often frustrated by the inherent complexities in each, and the shortage of quality data from field exercises. This thesis examines the feasibility of comparing these systems indirectly through the use of surrogate analytical models. A simple discrete time stochastic surrogate model is examined. Techniques for using the surrogate model to compare battle data are studied using simulated data from a simple combat model. Areas for further research are discussed. Combat models, Simulated annealing, Regression, Difference equations, Stochastic models.

Edition Notes

ContributionsBarr, Donald Roy
The Physical Object
Pagination82 p. ;
Number of Pages82
ID Numbers
Open LibraryOL25514762M

A constraint mapping approach to the structural optimization of an expensive model using surrogates. Optimization and Engineering 2, – () zbMATH CrossRef MathSciNet Google Scholar Redhe, M., Nilsson, L.: Optimization of the new Saab exposed to impact load using Cited by: 3 Combat Models Lanchester Combat Model Phase Plane Graphs The Lanchester Model with Reinforcements Hughes Aimed Fire Salvo Model Armstrong Salvo Model with Area Fire 4 The Spread of Infectious Diseases The S–I–R Model S–I–R with Vital Dynamics

Model Comparison Using the Deviance, 86 Residuals Comparing Observations to the Model Fit, 87 Fitting Generalized Linear Models, 88 The Newton–RaphsonAlgorithm Fits GLMs, 88 Wald, Likelihood-Ratio, and Score Inference Use the Likelihood Function, 89 Advantages of GLMs, 90 Problems, 90 4. Logistic Regression 99File Size: 2MB. Prior to using ComBat, the first PC in Run Two was significantly associated with the chip on which samples were run (pusing ComBat, the first and third PCs were significantly associated with As exposure group (pCited by:

Effect Plot. The weights of the linear regression model can be more meaningfully analyzed when they are multiplied by the actual feature values. The weights depend on the scale of the features and will be different if you have a feature that measures e.g. a person’s height and you .   Porter's 5 Forces examines the five forces that make a company competitive while helping identify its strengths and weaknesses. PESTLE is an analytical tool .


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Comparing combat models using analytical surrogates by Green, John R. Download PDF EPUB FB2

Comparing combat models using analytical surrogates. [John R Green] on *FREE* shipping on qualifying offers. This thesis examines the feasibility of comparing these systems indirectly through the use of surrogate analytical models. A simple discrete time stochastic surrogate model is examined.

Techniques for using the surrogate model to compare battle data are studied using simulated data from a simple combat model. Areas for further research are : John R. Green. “This book is devoted to the analysis of multifarious combat models, which are classified into nine groups.

Each chapter corresponds to one such class. The authors start with a description of commonly used assumptions, which considerably simplify examination. requires a basic knowledge of calculus, ordinary differential equations, probability theory, stochastic processes and by: The powerful techniques of modern nonlinear statistical mechanics are used to compare battalion scale combat computer models (including simulations and wargames) to exercise data.

This is necessary if large-scale combat computer models are to be extrapolated with Comparing combat models using analytical surrogates book to develop battle-management, C 3 and procurement decision-aids, and to improve by: Combat Models for RTS Games Alberto Uriarte and Santiago Onta n on´ Abstract Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or simulator) of the game at hand.

However, in some games such forward model is File Size: KB. The co-kriging based surrogate model is a promising tool to build such an approximation.

The idea is to improve the surrogate model by using fast and less accurate versions of the code. The development of a surrogate lower limb model, whose behaviour in comparison to a large number of cadaveric results is known, may provide a more powerful tool in predicting injury, since it can be used in conjunction with injury curves that have been developed through analysis of a Cited by: 5.

SURROGATE MODELLING IN MODEL-BASED OPTIMIZATION. AN INTRODUCTION Dimitri Solomatine Introduction This paper should be seen as an introduction and a brief tutorial in surrogate modelling. It can be also used by students who would like to. Surrogate models and the optimization of systems in the absence of algebraic models Nick Sahinidis Acknowledgments: Alison Cozad, David Miller, Zach Wilson Atharv Bhosekar, Luis Miguel Rios, Hua Zheng.

Carnegie CPU TIME COMPARISON 1 10 File Size: 2MB. Allows to remove batch effects and other unwanted variation in high-throughput experiment. SVA is a package containing several functions permitting to identify and build surrogate variables for large data sets.

Artifacts can be removed in three ways: (i) identification and estimation of surrogate variables, (ii) direct removal of known batch effect with ComBat and (iii) removal of batch effect Alternative names: Surrogate Variable Analysis, fSVA.

qualitative analysis of 35 surrogate interviews highlighted important surrogate-centric factors, such as a wish for family consensus, that were considered in addition to the patients’ preferences While these studies provide insight into the complexities of surrogate decision-making, it is still unclearCited by: 6.

Meta-analysis results are affected by which studies are included, what endpoints are evaluated, and what analytical methods are used to model the treatment effects. There is no consensus on what level of correlation between hazard ratios is required to consider PFS a useful surrogate for by: surrogate data test for nonlinearity can be applied to assess the presence of nonlin-ear correlations and subsequently model the nonlinear structure and attempt better forecasts.

The two surrogate data tests are presented in Section2. An extended discussion on the test for nonlinearity follows in Section3 including the algorithms for the. KM Survival Analysis The survival probability estimate of the censored data using the KM model with the parametric survival model is given by Figurebelow.

In comparing the KM survival curve with the parametric model using the probability residuals, we have found that the mean of the probability residual issample variance isFile Size: KB.

Using analytics for insUrance fraUD Detection Digital transformation 3 Traditionally, insurance companies use statistical models to identify fraudulent claims These models have their own disadvantages.

First, they use sampling methods to analyze data, which leads to one or more frauds going undetected. There is a penalty for not analyzing all File Size: 1MB. In the analysis of micropollutants from environmental samples, surrogates are often used to monitor recoveries in extraction and cleanup.

These surrogates are often isotope labelled analytes. Surrogate models for mixed discrete-continuous variables Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis Storlie, Herbert Lee Prepared by Sandia National Laboratories Albuquerque, New Mexico and Livermore, California Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation.

“This book is devoted to the analysis of multifarious combat models, which are classified into nine groups. Each chapter corresponds to one such class. The authors start with a description of commonly used assumptions, which considerably simplify examination.

requires a basic knowledge of calculus, ordinary differential equations. Combat Models started their plastic scale modeling journey in the Eighties of the previous century.

Based on our records the first release by Combat Models was roughly 40 years ago in the year products from Combat Models have no clear release year and are not shown in the above statistics.

need an analytical tool (e.g., model, simulation, or field test) to answer our basic question. We realized early on that a pen and paper analysis would not be able to capture the multitude of variables that can influence ground combat; we knew we needed a more robust tool.

One of our first observations was that the number of variables. A surrogate model is an engineering method used when an outcome of interest cannot be easily directly measured, [disputed – discuss] so a model of the outcome is used instead.

Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as a function of design variables.REVIEW ARTICLE /WR A review of surrogate models and their application to groundwater modeling M. J. Asher 1,2, B. F. W. Croke, A.

J. Jakeman1, and L. J. M. Peeters3 1Integrated Catchment Assessment and Management, National Centre for Groundwater Research and Training, Australian National University, Canberra, Australian Capital Territory, Australia, Cited by: A Review of Methods for Missing Data Therese D. Pigott Loyola University Chicago, Wilmette, IL, USA ABSTRACT This paper reviews methods for handling missing data in a research study.

Many researchers use ad hoc methods such as complete case analysis, available case analysis (pairwise deletion), or single-value imputation.