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Bayesian and Non-Bayesian Approaches to Statistical Inference: A Personal View

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Statistical Methods and Applications from a Historical Perspective

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

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Abstract

Bayesian and non-bayesian approaches to statistical inference are compared giving particular attention to the emerging field of causal statistical inference and causal statistical decision theory. After a brief review of the evolution of statistical inference, as extraction of information and identification of models from data, the problematic issues of causal inference and causal decision theory will be reviewed. The aim is to provide some basic ideas for unifying the different approaches and for strengthening the future of statistics as a discipline.

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Notes

  1. 1.

    On the contributions of Gini to the foundations of probability and statistical inference I strongly recommend a forthcoming paper by Piccinato (2011).

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Authors and Affiliations

  1. Department of Statistics, University of Florence, Florence, Italy

    Bruno Chiandotto

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  1. Bruno Chiandotto
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Correspondence to Bruno Chiandotto .

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Editors and Affiliations

  1. National Institute of Statistics, Istat Head of Office for Census Methods, Rome, Italy

    Fabio Crescenzi

  2. University of Bologna Department of Statistical Sciences, Bologna, Italy

    Stefania Mignani

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Chiandotto, B. (2014). Bayesian and Non-Bayesian Approaches to Statistical Inference: A Personal View. In: Crescenzi, F., Mignani, S. (eds) Statistical Methods and Applications from a Historical Perspective. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/978-3-319-05552-7_1

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