Library of American University of Madaba
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Robert, Christian.
Introducing Monte Carlo Methods with R [electronic resource] / by Christian Robert, George Casella. - XX, 284 p. online resource. - Use R .
Basic R Programming -- Random Variable Generation -- Monte Carlo Integration -- Controlling and Accelerating Convergence -- Monte Carlo Optimization -- Metropolis–Hastings Algorithms -- Gibbs Samplers -- Convergence 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 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. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis
9781441915764
Statistics.
Computer science.
Computer simulation.
Computer science--Mathematics.
Distribution (Probability theory).
Mathematical statistics.
Engineering mathematics.
Statistics.
Statistics and Computing/Statistics Programs.
Simulation and Modeling.
Computational Mathematics and Numerical Analysis.
Probability and Statistics in Computer Science.
Appl.Mathematics/Computational Methods of Engineering.
Probability Theory and Stochastic Processes.
QA276-280
519.5
Introducing Monte Carlo Methods with R [electronic resource] / by Christian Robert, George Casella. - XX, 284 p. online resource. - Use R .
Basic R Programming -- Random Variable Generation -- Monte Carlo Integration -- Controlling and Accelerating Convergence -- Monte Carlo Optimization -- Metropolis–Hastings Algorithms -- Gibbs Samplers -- Convergence 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 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. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis
9781441915764
Statistics.
Computer science.
Computer simulation.
Computer science--Mathematics.
Distribution (Probability theory).
Mathematical statistics.
Engineering mathematics.
Statistics.
Statistics and Computing/Statistics Programs.
Simulation and Modeling.
Computational Mathematics and Numerical Analysis.
Probability and Statistics in Computer Science.
Appl.Mathematics/Computational Methods of Engineering.
Probability Theory and Stochastic Processes.
QA276-280
519.5