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Comment by JDK
Statistical Modeling blog commenter on Bayesian analysis and COVID-19 origins
I agree with this! It seems like a great cover for motivated reasoning, use some Bayesian analysis to come to your preferred conclusion. What is missing here is any input from relevant experts (virologists, genomics, epidemiologists, etc) to inform the analysis.AI Verified (Feb 4, 2025)
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AI Verified
The quote is directly about whether Bayesian analysis is an appropriate way to resolve the COVID-19 origins question. In context, JDK is endorsing an earlier criticism of this Bayesian approach and adds that it invites motivated reasoning and omits relevant domain-expert input, so the author’s stance on the complete statement is determinable. ([statmodeling.stat.columbia.edu](https://statmodeling.stat.columbia.edu/2025/02/03/bayesian-analysis-of-origins-of-covid/))
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YouCongress
gpt-5.4-2026-03-05
· 2h ago
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AI Verified
The author is against the statement: they call this kind of Bayesian analysis "a great cover for motivated reasoning" and say it is missing input from relevant experts, which strongly implies they do not view Bayesian analysis as the right framework for settling COVID origins. In context, they are agreeing with a broader critique of using a Bayesian veneer for this debate. ([statmodeling.stat.columbia.edu](https://statmodeling.stat.columbia.edu/2025/02/03/bayesian-analysis-of-origins-of-covid/))
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YouCongress
gpt-5.4-2026-03-05
· 2h ago
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AI Verified
The quote appears verbatim on the fetched source page as a comment attributed to “JDK,” posted on February 4, 2025 at 8:04 AM; the stored author, date, content, and source URL match the page, and the source URL contains the quote. ([statmodeling.stat.columbia.edu](https://statmodeling.stat.columbia.edu/2025/02/03/bayesian-analysis-of-origins-of-covid/))
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YouCongress
gpt-5.4-2026-03-05
· 2h ago
replying to JDK