Introducing Monte Carlo Methods with R
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ISBN-13:
9781441915757
Veröffentl:
2009
Erscheinungsdatum:
10.12.2009
Seiten:
284
Autor:
Christian Robert
Gewicht:
479 g
Format:
233x154x20 mm
Sprache:
Englisch
Beschreibung:
Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here.
The first book to present modern Monte Carlo and Markov Chain Monte Carlo (MCMC) methods from a practical perspective through a guided implementation in the R language
Basic R Programming.- Random Variable Generation.- Monte Carlo Integration.- Controlling and Accelerating Convergence.- Monte Carlo Optimization.- Metropolis#x2013;Hastings Algorithms.- Gibbs Samplers.- Convergence Monitoring and Adaptation for MCMC Algorithms.
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