Introduction to Scientific Programming and Simulation Using R
This book has two principal aims: to teach scientific programming and to introduce stochastic modelling. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems. In the context of stochastic modelling, simulation is the numerical technique that enables us to analyse otherwise intractable models.
Simulation is also the best way we know of developing statistical intuition.
This book assumes that users have completed or are currently undertaking a first year university level calculus course. The material is suitable for first and second year science/engineering/commerce students and masters level students in applied disciplines. No prior knowledge of programming or probability is assumed.
It is possible to use the book for a first course on probability, with an emphasis on applications facilitated by simulation. Modern applied probability and statistics are numerically intensive, and we give an approach that integrates programming and probability right from the start.
We chose the programming language R because of its programming features. We do not describe statistical techniques as implemented in R (though many of them are admittedly quite remarkable), but rather show how to turn algorithms into code. Our intended audience is those who want to make tools, not just use them.
Complementing the book is a package, spuRs, containing most of the code and data we use. Instructions for installing it are given in the first chapter. In the back of the book we also provide an index of the programs developed in the text and a glossary of R commands.
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|May 30, 2020|
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