Prior Near-Ignorance for Inferences in the k-parameter Exponential Family

@inproceedings{Benavoli2014PriorNF,
  title={Prior Near-Ignorance for Inferences in the k-parameter Exponential Family},
  author={Alessio Benavoli and Marco Zaffalon},
  year={2014}
}
This paper proposes a model of prior ignorance about a multivariate variable based on a set of distributions M . In particular, we discuss four minimal properties that a model of prior ignorance should satisfy: invariance, near-ignorance, learning and convergence. Near-ignorance and invariance ensure that our prior model behaves as a vacuous model with respect to some statistical inferences (e.g., mean, credible intervals, etc.) and some transformation of the parameter space. Learning and… CONTINUE READING
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Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 27 references

A bounded derivative model for prior ignorance about a real-valued parameter

  • P. Walley
  • Scandinavian Journal of Statistics,
  • 1997
Highly Influential
10 Excerpts

Inferences from multinomial data: learning about a bag of marbles

  • P. Walley
  • Journal of the Royal Statistical Society. Series…
  • 1996
Highly Influential
20 Excerpts

Statistical Reasoning with Imprecise Probabilities

  • P. Walley
  • 1991
Highly Influential
15 Excerpts

Robust Bayesian credible intervals and prior ignorance

  • L. R. Pericchi, P. Walley
  • International Statistical Review,
  • 1991
Highly Influential
5 Excerpts

Asymptotic theory of statistics and probability

  • Anirban DasGupta
  • 2008

An introduction to the imprecise Dirichlet model for multinomial data

  • J.-M. Bernard
  • International Journal of Approximate Reasoning,
  • 2005
1 Excerpt

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