Titlebook: R by Example; Jim Albert,Maria Rizzo Book 20121st edition Springer Science+Business Media, LLC, part of Springer Nature 2012 R application

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Simulation Experiments,Simulation provides a straightforward way of approximating probabilities. We illustrate the use of two R functions in simulating some famous probability problems.

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Bayesian Modeling,The basic tenets of Bayesian inference are introduced. This includes the construction of a prior and the use of the posterior distribution to perform inferences. Simulation is helpful in summarizing posterior distributions and Markov chain Monte Carlo algorithms are useful in simulating from posterior distributions of non-familiar forms.

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Monte Carlo Methods, compute some expectations by the Monte Carlo method. These computations are helpful in comparing sampling properties of point estimators or evaluating probabilities of coverage of interval estimators. We illustrate the use of Markov chain Monte Carlo (MCMC) methods in simulating from sampling distributions.

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978-1-4614-1364-6Springer Science+Business Media, LLC, part of Springer Nature 2012

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