On nonparametric predictive inference and abjective Bayesianism

Coolen, F.P.A.

Abstract

This paper consists of three main parts. First, we give an introduction to Hill’s assumption A (n) and to theory of interval probability, and an overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A (n) and uses interval probability to quantify uncertainty. Thereafter, we illustrate NPI by introducing a variation to the assumption A (n), suitable for inference based on circular data, with applications to several data sets from the literature. This includes attention to comparison of two groups of circular data, and to grouped data. We briefly discuss such inference for multiple future observations. We end the paper with a discussion of NPI and objective Bayesianism.

Citation

Coolen, F. (2006). On nonparametric predictive inference and abjective Bayesianism. Journal of Logic, Language and Information, 15(1-2), 21-47. https://doi.org/10.1007/s10849-005-9005-7

• Journal Article Type: Article
• Publication Date: Jul 1, 2006
• Deposit Date: Jan 9, 2009
• Journal: Journal of Logic, Language and Information
• Print ISSN: 0925-8531
• Electronic ISSN: 1572-9583
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 1-2
• Pages: 21-47
• DOI: https://doi.org/10.1007/s10849-005-9005-7
• Keywords: Circular data, Exchangeability, Grouped data, Imprecise probabilities, Interval probability, Objective Bayesianism.
• Public URL: https://durham-repository.worktribe.com/output/1557151