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Introduction: New blog from the philosopher Deborah Mayo who I think agrees with me about many statistical issues although from a non-Bayesian perspective. But I disagree with her when she writes that certain criticisms of frequentist statistical methods “keep popping up (verbatim) in every Bayesian textbook and article on philosophical foundations.” I’ve written a couple of Bayesian textbooks and some articles on philosophical foundations, and I don’t think I do this! That said, I think Mayo has a lot to say, so I wouldn’t judge her whole blog (let alone her published work) based on that one intemperate statement.


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4 That said, I think Mayo has a lot to say, so I wouldn’t judge her whole blog (let alone her published work) based on that one intemperate statement. [sent-4, score-1.138]


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Introduction: New blog from the philosopher Deborah Mayo who I think agrees with me about many statistical issues although from a non-Bayesian perspective. But I disagree with her when she writes that certain criticisms of frequentist statistical methods “keep popping up (verbatim) in every Bayesian textbook and article on philosophical foundations.” I’ve written a couple of Bayesian textbooks and some articles on philosophical foundations, and I don’t think I do this! That said, I think Mayo has a lot to say, so I wouldn’t judge her whole blog (let alone her published work) based on that one intemperate statement.

2 0.27250966 1433 andrew gelman stats-2012-07-28-LOL without the CATS

Introduction: Mayo points me to this discussion [link fixed] on parsimony by philosopher Elliott Sober. I don’t really understand what he’s talking about but I am posting the link here because it might interest some of you. P.S. More discussion on this from Mayo here .

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Introduction: Deborah Mayo, Aris Spanos, and Kent Staley edited a special issue on the philosophy of Bayesian statistics for the journal Rationality, Markets and Morals. Here are the contents : David Cox and Deborah G. Mayo, “Statistical Scientist Meets a Philosopher of Science: A Conversation” Deborah G. Mayo, “Statistical Science and Philosophy of Science: Where Do/Should They Meet in 2011 (and Beyond)?” Stephen Senn, “You May Believe You Are a Bayesian But You Are Probably Wrong” Andrew Gelman, “ Induction and Deduction in Bayesian Data Analysis “ Jan Sprenger, “The Renegade Subjectivist: Jose Bernardo’s Objective Bayesianism” Aris Spanos. “Foundational Issues in Statistical Modeling: Statistical Model Specification and Validation” David F. Hendry, “Empirical Economic Model Discovery and Theory Evaluation” Larry Wasserman, “Low Assumptions, High Dimensions” For some reason, not all the articles are yet online, but it says they’re coming soon. In the meantime, you ca

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Introduction: Deborah Mayo collected some reactions to my recent article , Induction and Deduction in Bayesian Data Analysis. I’m pleased that that everybody (philosopher Mayo, applied statistician Stephen Senn, and theoretical statistician Larry Wasserman) is so positive about my article and that nobody’s defending the sort of hard-core inductivism that’s featured on the Bayesian inference wikipedia page. Here’s the Wikipedia definition, which I disagree with: Bayesian inference uses aspects of the scientific method, which involves collecting evidence that is meant to be consistent or inconsistent with a given hypothesis. As evidence accumulates, the degree of belief in a hypothesis ought to change. With enough evidence, it should become very high or very low. . . . Bayesian inference uses a numerical estimate of the degree of belief in a hypothesis before evidence has been observed and calculates a numerical estimate of the degree of belief in the hypothesis after evidence has been obse

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Introduction: Larry Brownstein writes: I read your article on induction and deduction and your comments on Deborah Mayo’s approach and thought you might find the following useful in this discussion. It is Wesley Salmon’s Reality and Rationality (2005). Here he argues that Bayesian inferential procedures can replace the hypothetical-deductive method aka the Hempel-Oppenheim theory of explanation. He is concerned about the subjectivity problem, so takes a frequentist approach to the use of Bayes in this context. Hardly anyone agrees that the H-D approach accounts for scientific explanation. The problem has been to find a replacement. Salmon thought he had found it. I don’t know this book—but that’s no surprise since I know just about none of the philosophy of science literature that came after Popper, Kuhn, and Lakatos. That’s why I collaborated with Cosma Shalizi. He’s the one who connected me to Deborah Mayo and who put in the recent philosophy references in our articles. Anyway, I’m pa

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Introduction: New blog from the philosopher Deborah Mayo who I think agrees with me about many statistical issues although from a non-Bayesian perspective. But I disagree with her when she writes that certain criticisms of frequentist statistical methods “keep popping up (verbatim) in every Bayesian textbook and article on philosophical foundations.” I’ve written a couple of Bayesian textbooks and some articles on philosophical foundations, and I don’t think I do this! That said, I think Mayo has a lot to say, so I wouldn’t judge her whole blog (let alone her published work) based on that one intemperate statement.

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Introduction: My paper with Christian Robert, “Not Only Defended But Also Applied”: The Perceived Absurdity of Bayesian Inference , was recently published in The American Statistician, along with discussions by Steve Fienberg, Steve Stigler, Deborah Mayo, and Wesley Johnson, and our rejoinder, The Anti-Bayesian Moment and Its Passing . These articles revolved around the question of why the great probabilist William Feller, in his classic book on probability (“Feller, Volume 1,” as it is known), was so intemperately anti-Bayesian. We located Feller’s attitude within a post-WW2 “anti-Bayesian moment” in which Bayesian inference was perceived as a threat to the dominance of non-Bayesian methods, which were mature enough to have solved problems yet new enough to still appear to have limitless promise. Howard Wainer read this. Howard is a friend who has a longstanding interest in the history of statistics and who also has known a lot of important statisticians over the years. Howard writes: O

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