{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T09:47:21Z","timestamp":1785836841557,"version":"3.56.0"},"reference-count":108,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We argue that the core methods of modern machine learning\u2014conformal prediction, large language models and in-context learning, and generative\/diffusion models\u2014are not rivals to Bayesian inference but implementations of it, almost always of its predictive (de Finetti) form rather than its parameter-centric (prior-to-posterior) form. (1) Background: A quarter-century after Breiman contrasted the \u201cdata-modeling\u201d and \u201calgorithmic\u201d cultures of statistics, we revisit that dichotomy and argue that the predictive view dissolves it\u2014both cultures target the one-step-ahead density p(yn+1\u2223y1:n), differing only in how they compute it. (2) Methods: We organize the modern toolkit around this predictive object and around amortization\u2014replacing per-dataset inference with a single map learned by simulation\u2014using Generative Bayesian Computation (GBC) as the connective spine. (3) Results: Conformal prediction, autoregressive language models, prior-data fitted networks, and score-based diffusion are each shown to construct, calibrate, or sample from the predictive object, summarized in a single \u201cRosetta\u201d table; because all are fit by proper scoring rules\u2014equivalently, by Bregman divergences\u2014the information-theoretic frame is the natural unifier. (4) Conclusions: The equivalence is exact in idealized limits, asymptotic under exchangeability and its martingale relaxations, and measurably approximate for trained models\u2014a three-grade taxonomy that we make explicit, row by row, and that bounds the thesis: prediction is not attribution, and the predictive view is deliberately silent about causal structures.<\/jats:p>","DOI":"10.3390\/e28080869","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T15:51:23Z","timestamp":1785772283000},"page":"869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Everything Is Prediction: Modern Machine Learning as Bayesian Inference"],"prefix":"10.3390","volume":"28","author":[{"given":"Nicholas G.","family":"Polson","sequence":"first","affiliation":[{"name":"Booth School of Business, University of Chicago, Chicago, IL 60637, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6618-2965","authenticated-orcid":false,"given":"Vadim","family":"Sokolov","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7854-7837","authenticated-orcid":false,"given":"Refik","family":"Soyer","sequence":"additional","affiliation":[{"name":"Department of Decision Sciences, School of Business, George Washington University, Washington, DC 20052, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical modeling: The two cultures","volume":"16","author":"Breiman","year":"2001","journal-title":"Stat. 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