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Bibliographic Details
Main Authors: Bissiri, Pier Giovanni, Holmes, Chris, Walker, Stephen G.
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.20071
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author Bissiri, Pier Giovanni
Holmes, Chris
Walker, Stephen G.
author_facet Bissiri, Pier Giovanni
Holmes, Chris
Walker, Stephen G.
contents This paper is concerned with the construction of prior free posterior distributions which rely on the use of one step ahead predictive distribution functions. These are typically more straightforward to motivate than prior distributions. Recent interest has been with Hill's $A_n$ prediction model through what has become known as conformal prediction. This model predicts the next observation to lie with equal probability in the intervals created by the observed data. The prediction model generates complete data sets which can be used to provide posterior inference on any statistic of interest.
format Preprint
id arxiv_https___arxiv_org_abs_2603_20071
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Posterior inference via Hill's prediction model
Bissiri, Pier Giovanni
Holmes, Chris
Walker, Stephen G.
Methodology
Statistics Theory
This paper is concerned with the construction of prior free posterior distributions which rely on the use of one step ahead predictive distribution functions. These are typically more straightforward to motivate than prior distributions. Recent interest has been with Hill's $A_n$ prediction model through what has become known as conformal prediction. This model predicts the next observation to lie with equal probability in the intervals created by the observed data. The prediction model generates complete data sets which can be used to provide posterior inference on any statistic of interest.
title Posterior inference via Hill's prediction model
topic Methodology
Statistics Theory
url https://arxiv.org/abs/2603.20071