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Learn How to Program Stochastic Models
Highly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data.
The book’s four parts teach:
- Core knowledge of R and programming concepts
- How to think about mathematics from a numerical point of view, including the application of these concepts to root finding, numerical integration, and optimisation
- Essentials of probability, random variables, and expectation required to understand simulation
- Stochastic modelling and simulation, including random number generation and Monte Carlo integration
In a new chapter on systems of ordinary differential equations (ODEs), the authors cover the Euler, midpoint, and fourth-order Runge-Kutta (RK4) schemes for solving systems of first-order ODEs. They compare the numerical efficiency of the different schemes experimentally and show how to improve the RK4 scheme by using an adaptive step size.
Another new chapter focuses on both discrete- and continuous-time Markov chains. It describes transition and rate matrices, classification of states, limiting behaviour, Kolmogorov forward and backward equations, finite absorbing chains, and expected hitting times. It also presents methods for simulating discrete- and continuous-time chains as well as techniques for defining the state space, including lumping states and supplementary variables.
Building readers’ statistical intuition, Introduction to Scientific Programming and Simulation Using R, Second Edition shows how to turn algorithms into code. It is designed for those who want to make tools, not just use them. The code and data are available for download from CRAN.
- Sales Rank: #460603 in Books
- Published on: 2014-06-12
- Original language: English
- Number of items: 1
- Dimensions: 9.40" h x 1.30" w x 6.20" l, 2.16 pounds
- Binding: Hardcover
- 606 pages
Review
"Computation has become so central to the field of statistics that any practicing statistician must have a basic understanding of scientific programming and stochastic modeling. Introduction to Scientific Programming and Simulation Using R provides an excellent entry-level text on the subject. This is a well written and well-designed book that will appeal to a wide readership and prove useful for several different types of courses. It provides a very good introduction to programming using the R language that has become widely used in statistical education and practice. It also introduces the fundamental tools needed for stochastic modeling: numerical analysis, probability, and simulation. -Christopher H. Schmid, Journal of the American Statistical Association
Praise for the First Edition:
"Overall, the authors have produced a highly readable text. As prerequisites do not go beyond first-year calculus, the book should appeal to a wide audience; it should also be eminently suitable for self-study. On a somewhat larger scale, it may help to further establish R as a kind of Swiss Army knife for computational science. I strongly recommend it."
―C. Kleiber, Universit�t Basel, Basel, Switzerland, in Statistical Papers, March 2012
This book is a good resource for someone who wants to learn R and use R for statistical computing and graphics. It will also serve well as a textbook or a reference book for students in a course related to computational statistics.
―Hon Keung Tony Ng, Technometrics, May 2011
… a very coherent and useful account of its chosen subject matter. … The programming section … is more comprehensive than Braun & Murdoch (2007), but more accessible than Venables & Ripley (2000). … The book deserves a place on university library shelves … One very useful feature of the book is that nearly every chapter has a set of exercises. There are also plenty of well-chosen examples throughout the book that are used to explain the material. I also appreciated the clear and attractive programming style of the R code presented in the book. I found very little in the way of typos or solecisms. … I can strongly recommend the book for its intended audience. If I ever again have to teach our stochastic modelling course, I will undoubtedly use some of the exercises and examples from Scientific Programming and Simulation Using R.
―David Scott, Australian & New Zealand Journal of Statistics, 2011
It is not often that I think that a statistics text is one that most scientifc statisticians should have in their personal libraries. Introduction to Scientific Programming and Simulation Using R is such a text. … This text provides scientific researchers with a working knowledge of R for both reviewing and for engaging in the statistical evaluation of scientific data. …It is particularly useful for understanding and developing modeling and simulation software. I highly recommend the text, finding it to be one of the most useful books I have read on the subject.
―Journal of Statistical Software, September 2010, Volume 36
The authors have written an excellent introduction to scientific programming with R. Their clear prose, logical structure, well-documented code and realistic examples made the book a pleasure to read. One particularly useful feature is the chapter of cases studies at the end, which not only demonstrates complete analyses but also acts as a pedagogical tool to review and integrate material introduced throughout the book. … I would strongly recommend this book for readers interested in using R for simulations, particularly for those new to scientific programming or R. It is also very student-friendly and would be suitable either as a course textbook or for self-study.
―Significance, September 2009
I think that the techniques of scientific programming presented will soon enable the novice to apply statistical models to real-world problems. The writing style is easy to read and the book is suitable for private study. If you have never read a book on scientific programming and simulation, then I recommend that you start with this one.
―International Statistical Review, 2009
Most helpful customer reviews
5 of 5 people found the following review helpful.
Better if you already have some exposure to numerical integration and stochastic modeling
By OnceMore
Although it's stated in the book's preface that the prerequisites don't go beyond a first course in calculus, in my opinion, you'll get more out of this book if you already have some exposure to numerical integration and stochastic modeling, because the authors go through these topics fairly quickly, and without lengthy explanations.
The book is divided into four parts. Part One consists of very good tutorials on R programming concepts, including how to write performant code. Part Two focuses on Numerical Techniques, such as finding the maximum or minimum of a possibly multivariate function. For each class of problems, the authors discuss up to three techniques for finding solutions, listing the pro's and con's for each technique, and also providing code examples, before pointing out what's available in R that provides the same functionalities.
Part Three covers probability, discrete and continuous random variables, and the most commonly encountered probability distributions for discrete and continuous random variables (e.g., binomial, Poisson, uniform, normal). Part Four discusses Simulations, providing code examples for phenomena such as epidemics, etc.
Each chapter ends with exercises, but the solutions to them are not included in the book. Code and data used in this book are available for download through standard R facilities, and instructions on how to do this are provided in the book.
4 of 4 people found the following review helpful.
Introduction to scientific techniques in R examples
By Eugene Tenenbaum Reluctant Reader
ISBN 1466569999 (Introduction to Scientific Programming and Simulation Using R, 2nd ed.) is an excellent compact introduction to R for near beginners in programming, but not so much in statistics, perfectly complementing more advanced Handbook of Statistical Analyses. It starts with the core knowledge in 6 chapters on installing, calculating, programming, input/output, functions, and data. They are followed by chapters on graphics, accuracy & efficiency, root-finding, optimization, probability, variables, estimation, simulation, Monte-Carlo, variance, cases, and projects, as in the 1st ed., and additionally on systems of ordinary differential equations and Markov chains not in the 1st ed..
The text is full of useful examples well teaching R to solve scientific problems of explained techniques and involved math described individually in separate chapters. The descriptions are systematic explanations in the tutorial style: introduction of data, R example to purify data, and R example to find the data significance. The book is well printed on good paper, and hard-bound without a dust jacket. The covers are strong and flexibly jointed. It could serve adepts at R as a reference work too, but at a price.
3 of 3 people found the following review helpful.
An excellent introduction to R and Numeric Programming
By Ian K.
The R programming language and mathematics environment is heavily used in the University of Washington's Computational Finance and Risk Management Masters program where I got my Masters degree. I have also been using R intensively for computer network data analysis, machine learning and investment portfolio construction. While R has its faults, I've definitely joined the Use R! crowd.
R is a programming language. Like other mathematical programming environments, R is built around vectors and matrices. In fact, there are no scalar values in R, only vectors (a scalar is a single element vector). But R is also a extensible open source environment. In the introduction, the authors write "R is under constant review". To use R effectively you need to gain an understanding not only of the R programming language, but also some of the constantly increasing number of libraries that are available in the R environment.
Introduction to Scientific Programming and Simulation Using R is a good place to start your journey as an R programmer. The book has one of the best concise introduction to R that I've seen (I also like Norman Matloff's The Art of R Programming: A Tour of Statistical Software Design). Although I am an experienced R programmer, reading this first part of the book explained R's factor data structure and the tapply function, which I had not understood before.
The book is designed to be a text book. I think that it would make a good text book for a class in numeric analysis using R. In addition to the excellent introduction to R there are good chapters on root finding, numeric integration and optimization.
R includes several optimization packages for univariate and multivariate optimzation. The optimization chapter provides a background in optimization which will educate the reader in how optimization is performed. Beyond mentioning R's optim package, the book doesn't cover the application of R's optimization software.
The book also has an excellent and readable chapter on Markov chains. The authors provide a number of examples throughout the chapter, which helps the reader understand how Markov models might be applied. There are also good introductory chapters on Monte Carlo simulation and variance reduction for Monte Carlo methods.
In the preface the authors write "It is possible to use the book for a first course on probability". I have to respectfully disagree. I found the chapters on probability and random variables the weakest parts of the book (this is the reason that I have given the book four, rather than five starts).
There are a number of books that would be better for an introductory statistics course, including Introductory Statistics with R and the Modern Applied Statistics with S (R is based on the earlier S+ and R includes the MASS package for this book).
For computational finance a good book is Statistics and Data Analysis for Financial Engineering
For those of us who are no longer students, the book is a good reference on R. The sections on numerical techniques are also useful references to have on your shelf.
The authors have a small web site that goes along with the book. They have also published an R package on the R site CRAN, which can be downloaded into R. The package and data sets can also be downloaded from the web site. The web site includes the book's table of contents, which unfortunately is not included here.
The challenge in writing an introductory book is deciding what to put in, what to leave out and then to write clearly about the included topics. The authors have done an admirable job in providing an introduction to numeric programming and simulation.
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