Abstract
Under the Bayesian paradigm to statistical inference the posterior probability distribution contains in principle all relevant information. All statistical inference can be deduced from the posterior distribution by reporting appropriate summaries. This coherent nature of Bayesian inference can give rise to problems when the implied posterior summaries are unduly sensitive to some detail choices of the model. This chapter discusses summaries and diagnostics that highlight such sensitivity and ways to choose a prior probability model to match some desired (frequentist) summaries of the implied posterior inference.
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© 2010 Springer New York
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Chen, MH., Dey, D.K., Müller, P., Sun, D., Ye, K. (2010). Bayesian Influence and Frequentist Interface. In: Chen, MH., Müller, P., Sun, D., Ye, K., Dey, D. (eds) Frontiers of Statistical Decision Making and Bayesian Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6944-6_7
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DOI: https://doi.org/10.1007/978-1-4419-6944-6_7
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Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-6943-9
Online ISBN: 978-1-4419-6944-6
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