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Mastering Scientific Computing with R

You're reading from   Mastering Scientific Computing with R Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783555253
Length 432 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Programming with R 2. Statistical Methods with R FREE CHAPTER 3. Linear Models 4. Nonlinear Methods 5. Linear Algebra 6. Principal Component Analysis and the Common Factor Model 7. Structural Equation Modeling and Confirmatory Factor Analysis 8. Simulations 9. Optimization 10. Advanced Data Management Index

Summary


In this chapter, you learned how to perform basic simulations. We showed you how to generate pseudorandom numbers from a variety of common probability distributions. We also showed you how to perform Monte Carlo simulations including an overview of the mc2d package, and how it can be used to perform one- and two-dimensional Monte Carlo simulations in risk analysis. We also showed you how Monte Carlo methods can be applied to estimate integrals. Next, we demonstrated how importance sampling can be used to improve integral estimates. Then, we showed you how to generate random variables from unknown distributions using the rejection sampling method. Finally, we briefly showed you how simulation can be used to model physical systems by looking at Brownian motion in one- and two-dimensions. Now that you are familiar with the generation of random variables and Monte Carlo simulations, we are ready to move on to the next chapter, where we will show you how to perform numerical optimization...

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