1 Basic R Programming 2 Random Variable Generation 3 Monte Carlo Integration 4 Controlling and Accelerating Convergence 5 Monte Carlo Optimization 6 Metropolis{Hastings Algorithms 7 Gibbs Samplers 8 Monitoring and Adaptation for MCMC Algorithms
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 intuitio
This book 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. Table of Contents Chapte
