ABSTRACT

Learn How to Program Stochastic ModelsHighly 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,

part |2 pages

I Programming

chapter 1|8 pages

Setting up

chapter 2|18 pages

R as a calculating environment

chapter 3|20 pages

Basic programming

chapter 4|12 pages

4I/O: Input and output

chapter 5|26 pages

Programming with functions

chapter 6|26 pages

Sophisticated data structures

chapter 7|18 pages

Better graphics

chapter 8|30 pages

Pointers to further programming techniques

part |2 pages

II Numerical techniques

chapter 9|18 pages

Numerical accuracy and program efficiency

chapter 10|22 pages

Root-finding

chapter 11|16 pages

Numerical integration

chapter 12|26 pages

Optimisation

chapter 13|24 pages

Systems of ordinary differential equations

part |2 pages

PART III: Probability and statistics

chapter 14|14 pages

Probability

chapter 15|26 pages

Random variables

chapter 16|14 pages

Discrete random variables

chapter 17|22 pages

Continuous random variables

chapter 18|26 pages

Parameter estimation

chapter 19|68 pages

Markov chains

part |2 pages

PART IV: Simulation

chapter 20|24 pages

Simulation

chapter 21|8 pages

Monte Carlo integration

chapter 22|14 pages

Variance reduction

chapter 23|44 pages

Case studies

chapter 24|36 pages

Student projects