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Introducing Monte Carlo Methods with R (Use R!)

Amazon.com Price:  $53.24 (as of 01/05/2019 13:24 PST- Details)

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Description

Computational techniques based on simulation have now turn into an essential a part of the statistician’s toolbox. It is thus a very powerful to provide statisticians with a practical understanding of those methods, and there is not any 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. Whilst this book constitutes a comprehensive remedy of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). In a similar way, the more exploratory and less stable solutions aren’t 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. Whilst 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 to be had as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, regulate theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader.


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