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082 0 4 |a 502.85/53  |2 23 
049 |a UAMI 
100 1 |a Jones, Owen  |q (Owen Dafydd),  |e author. 
245 1 0 |a Introduction to scientific programming and simulation using R /  |c Owen Jones, Robert Maillardet, and Andrew Robinson. 
246 3 0 |a Scientific programming and simulation using R 
250 |a Second edition. 
264 1 |a Boca Raton, FL :  |b CRC Press,  |c [2014] 
264 4 |c ©2014 
300 |a 1 online resource (xxiv, 573 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Chapman & Hall/CRC The R series 
588 0 |a Print version record. 
504 |a Includes bibliographical references. 
520 8 |a Annotation  |b 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. 
505 0 |a I. Programming. Setting up -- R as a calculating environment -- Basic programming -- I/O: input and output -- Programming with functions -- Sophisticated data structures -- Better graphics -- Pointers to further programming techniques -- II. Numerical techniques. Numerical accuracy and program efficiency -- Root-finding -- Numerical integration -- Optimisation -- III. Probability and statistics. Probability -- Random variables -- Discrete random variables -- Continuous random variables -- Parameter estimation -- IV. Simulation -- Monte-Carlo integration -- Variance reduction -- Cases studies -- Student projects -- Glossary of R commands -- Programs and functions developed in the text. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Science  |x Data processing. 
650 0 |a Science  |x Computer simulation. 
650 0 |a Stochastic processes  |x Mathematical models. 
650 0 |a Numerical analysis  |x Data processing. 
650 0 |a R (Computer program language) 
650 0 |a Computer programming. 
650 6 |a Sciences  |x Informatique. 
650 6 |a Sciences  |x Simulation par ordinateur. 
650 6 |a Processus stochastiques  |x Modèles mathématiques. 
650 6 |a Analyse numérique  |x Informatique. 
650 6 |a R (Langage de programmation) 
650 6 |a Programmation (Informatique) 
650 7 |a computer programming.  |2 aat 
650 7 |a SCIENCE  |x General.  |2 bisacsh 
650 7 |a Computer programming  |2 fast 
650 7 |a Numerical analysis  |x Data processing  |2 fast 
650 7 |a R (Computer program language)  |2 fast 
650 7 |a Science  |x Computer simulation  |2 fast 
650 7 |a Science  |x Data processing  |2 fast 
650 7 |a Stochastic processes  |x Mathematical models  |2 fast 
700 1 |a Maillardet, Robert,  |e author. 
700 1 |a Robinson, Andrew  |q (Andrew P.),  |e author. 
776 0 8 |i Print version:  |a Jones, Owen (Owen Dafydd).  |t Introduction to scientific programming and simulation using R.  |b Second edition.  |d Boca Raton, FL : CRC Press, [2014]  |z 9781466570016  |w (OCoLC)885074437 
830 0 |a Chapman & Hall/CRC the R series (CRC Press) 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781466569997/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
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