Introduction to Scientific Programming and Simulation Using R
eBook - PDF

Introduction to Scientific Programming and Simulation Using R

  1. 606 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Introduction to Scientific Programming and Simulation Using R

About this book

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,

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Yes, you can access Introduction to Scientific Programming and Simulation Using R by Owen Jones,Robert Maillardet,Andrew Robinson in PDF and/or ePUB format, as well as other popular books in Mathematics & Mathematics General. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Front Cover
  2. Chapman & Hall/CRCThe R Series
  3. Published Titles
  4. Contents
  5. Preface
  6. How to use this book
  7. PART I: Programming
  8. CHAPTER 1: Setting up
  9. CHAPTER 2: R as a calculating environment
  10. CHAPTER 3: Basic programming
  11. CHAPTER 4: I/O: Input and output
  12. CHAPTER 5: Programming with functions
  13. CHAPTER 6: Sophisticated data structures
  14. CHAPTER 7: Better graphics
  15. CHAPTER 8: Pointers to further programming techniques
  16. PART II: Numerical techniques
  17. CHAPTER 9: Numerical accuracy and program efficiency
  18. CHAPTER 10: Root-finding
  19. CHAPTER 11: Numerical integration
  20. CHAPTER 12: Optimisation
  21. CHAPTER 13: Systems of ordinary differential equations
  22. PART III: Probability and statistics
  23. CHAPTER 14: Probability
  24. CHAPTER 15: Random variables
  25. CHAPTER 16: Discrete random variables
  26. CHAPTER 17: Continuous random variables
  27. CHAPTER 18: Parameter estimation
  28. CHAPTER 19: Markov chains
  29. PART IV: Simulation
  30. CHAPTER 20: Simulation
  31. CHAPTER 21: Monte Carlo integration
  32. CHAPTER 22: Variance reduction
  33. CHAPTER 23: Case studies
  34. CHAPTER 24: Student projects
  35. Glossary of R commands
  36. Programs and functions developed in the text
  37. Back Cover