Comment by Anonymous

Anything claiming to be “Bayesian” but not doing actual Bayesian statistics (fitting models to data and then doing an inference from a posterior distribution, justifying all steps especially the prior) is untrustworthy. I have never seen a convincing example of it, and I don’t think there can be.
AI Verified (Feb 3, 2025)
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AI Verified Relevant: on the source page, this quote is a direct criticism of the Bayesian COVID-origins analysis being discussed, saying purported Bayesian arguments of this kind are untrustworthy and warning against using made-up numerical odds, so opposition to Bayesian analysis as the right framework is substantially more likely than support. ([statmodeling.stat.columbia.edu](https://statmodeling.stat.columbia.edu/2025/02/03/bayesian-analysis-of-origins-of-covid/)) · YouCongress gpt-5.4-2026-03-05 · 58min ago
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AI Verified The quote is plainly skeptical: it says purportedly “Bayesian” arguments of this kind are “untrustworthy” unless they do full posterior-based modeling, and the speaker says they have “never seen a convincing example.” In the source post, the surrounding context is also skeptical, saying “It’s not clear to me that Bayesian inference is the right way” to handle this problem. So the most likely position is opposition to the claim that Bayesian analysis is the right framework for settling COVID origins. ([statmodeling.stat.columbia.edu](https://statmodeling.stat.columbia.edu/2025/02/03/bayesian-analysis-of-origins-of-covid/)) · YouCongress gpt-5.4-2026-03-05 · 57min ago

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AI Verified Verified. The fetched StatModeling post for February 3, 2025 contains an “Anonymous” comment dated February 3, 2025 at 11:22 PM, and the quoted text appears there verbatim. The stored author, date, content, and source URL are consistent with the source. · YouCongress gpt-5.4-2026-03-05 · 1h ago
replying to Anonymous