Internalist Reliabilism in Statistics and Machine Learning: Thoughts on Jun Otsuka's Thinking about Statistics

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1. Verfasser: Lin, Hanti
Format: Preprint
Veröffentlicht: 2024
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_version_ 1866909414119702528
author Lin, Hanti
author_facet Lin, Hanti
contents Otsuka (2023) argues for a correspondence between data science and traditional epistemology: Bayesian statistics is internalist; classical (frequentist) statistics is externalist, owing to its reliabilist nature; model selection is pragmatist; and machine learning is a version of virtue epistemology. Where he sees diversity, I see an opportunity for unity. In this article, I argue that classical statistics, model selection, and machine learning share a foundation that is reliabilist in an unconventional sense that aligns with internalism. Hence a unification under internalist reliabilism.
format Preprint
id arxiv_https___arxiv_org_abs_2412_02367
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Internalist Reliabilism in Statistics and Machine Learning: Thoughts on Jun Otsuka's Thinking about Statistics
Lin, Hanti
Other Statistics
Otsuka (2023) argues for a correspondence between data science and traditional epistemology: Bayesian statistics is internalist; classical (frequentist) statistics is externalist, owing to its reliabilist nature; model selection is pragmatist; and machine learning is a version of virtue epistemology. Where he sees diversity, I see an opportunity for unity. In this article, I argue that classical statistics, model selection, and machine learning share a foundation that is reliabilist in an unconventional sense that aligns with internalism. Hence a unification under internalist reliabilism.
title Internalist Reliabilism in Statistics and Machine Learning: Thoughts on Jun Otsuka's Thinking about Statistics
topic Other Statistics
url https://arxiv.org/abs/2412.02367