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:
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.
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Owen Jones, Robert Maillardet, Andrew Robinson
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Zustand: New. Owen Jones, Robert Maillardet, Andrew RobinsonLearn How to Program Stochastic ModelsHighly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an . Artikel-Nr. 595957028
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Buch. Zustand: Neu. Neuware - With new chapters on ODEs and Markov chains, the second edition of this highly recommended, best-selling book introduces scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data. Requiring no prior knowledge of programming or probability, the book shows them how to turn algorithms into code. It includes case studies that demonstrate the simulation techniques as well as numerous student projects and exercises. Artikel-Nr. 9781466569997
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