In this chapter, we introduce the use of R in summarizing the posterior distributions for several single-parameter models. We begin by describing Bayesian inference for a variance for a normal population and inference for a Poisson mean when informative prior information is available. For both problems, summarization of the posterior distribution is facilitated by the use of R functions to compute and simulate distributions from the exponential family. In Bayesian analyses, one may have limited beliefs about a parameter and there may be several priors that provide suitable matches to these beliefs. In estimating a normal mean, we illustrate the use of two distinct priors in modeling beliefs, and show that inferences may or may not be sensitive to the choice of prior.
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© 2007 Springer Science+Business Media, LLC
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(2007). Single-Parameter Models. In: Albert, J. (eds) Bayesian Computation with R. Use R!. Springer, New York, NY. https://doi.org/10.1007/978-0-387-71385-4_3
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DOI: https://doi.org/10.1007/978-0-387-71385-4_3
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-71384-7
Online ISBN: 978-0-387-71385-4
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