andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1259 knowledge-graph by maker-knowledge-mining
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Introduction: Hykel Hosni noticed this bit from the Lindley Prize page of the Society for Bayesan Analysis: Lindley became a great missionary for the Bayesian gospel. The atmosphere of the Bayesian revival is captured in a comment by Rivett on Lindley’s move to University College London and the premier chair of statistics in Britain: “it was as though a Jehovah’s Witness had been elected Pope.” From my perspective, this was amusing (if commonplace): a group of rationalists jocularly characterizing themselves as religious fanatics. And some of this is in response to intense opposition from outsiders (see the Background section here ). That’s my view. I’m an insider, a statistician who’s heard all jokes about religious Bayesians, from Bayesian and non-Bayesian statisticians alike. But Hosni is an outsider, and here’s how he sees the above-quoted paragraph: Research, however, is not a matter of faith but a matter of arguments, which should always be evaluated with the utmost intellec
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1 Hykel Hosni noticed this bit from the Lindley Prize page of the Society for Bayesan Analysis: Lindley became a great missionary for the Bayesian gospel. [sent-1, score-0.114]
2 The atmosphere of the Bayesian revival is captured in a comment by Rivett on Lindley’s move to University College London and the premier chair of statistics in Britain: “it was as though a Jehovah’s Witness had been elected Pope. [sent-2, score-0.577]
3 ” From my perspective, this was amusing (if commonplace): a group of rationalists jocularly characterizing themselves as religious fanatics. [sent-3, score-0.331]
4 And some of this is in response to intense opposition from outsiders (see the Background section here ). [sent-4, score-0.228]
5 I’m an insider, a statistician who’s heard all jokes about religious Bayesians, from Bayesian and non-Bayesian statisticians alike. [sent-6, score-0.215]
6 But Hosni is an outsider, and here’s how he sees the above-quoted paragraph: Research, however, is not a matter of faith but a matter of arguments, which should always be evaluated with the utmost intellectual honesty. [sent-7, score-0.41]
7 what academics constantly owe to society, is the moral obligation of refraining from dishonest nonsense, of which the above quoted passage is a despicable example. [sent-11, score-0.657]
8 I’m fascinated to see that an old joke can be so completely misperceived from the outside. [sent-12, score-0.179]
9 ” Of course, he could be himself joking by writing that. [sent-16, score-0.096]
10 It’s notoriously difficult to convey intonation in typed speech. [sent-17, score-0.274]
11 I’d like to stay on Hosni’s good side, given that he wrote a very nice summary of my recent Rationality, Markets and Morals article on induction and deduction in Bayesian data analysis. [sent-20, score-0.162]
12 Hosni seems completely up to date on Bayesian philosophy, not so much on statistics in-jokes. [sent-21, score-0.085]
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Introduction: Hykel Hosni noticed this bit from the Lindley Prize page of the Society for Bayesan Analysis: Lindley became a great missionary for the Bayesian gospel. The atmosphere of the Bayesian revival is captured in a comment by Rivett on Lindley’s move to University College London and the premier chair of statistics in Britain: “it was as though a Jehovah’s Witness had been elected Pope.” From my perspective, this was amusing (if commonplace): a group of rationalists jocularly characterizing themselves as religious fanatics. And some of this is in response to intense opposition from outsiders (see the Background section here ). That’s my view. I’m an insider, a statistician who’s heard all jokes about religious Bayesians, from Bayesian and non-Bayesian statisticians alike. But Hosni is an outsider, and here’s how he sees the above-quoted paragraph: Research, however, is not a matter of faith but a matter of arguments, which should always be evaluated with the utmost intellec
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Introduction: Sam Seaver writes: I [Seaver] happened to be reading an ironic article by Karl Friston when I learned something new about frequentist vs bayesian, namely Lindley’s paradox, on page 12. The text is as follows: So why are we worried about trivial effects? They are important because the probability that the true effect size is exactly zero is itself zero and could cause us to reject the null hypothesis inappropriately. This is a fallacy of classical inference and is not unrelated to Lindley’s paradox (Lindley 1957). Lindley’s paradox describes a counterintuitive situation in which Bayesian and frequentist approaches to hypothesis testing give opposite results. It occurs when; (i) a result is significant by a frequentist test, indicating sufficient evidence to reject the null hypothesis d=0 and (ii) priors render the posterior probability of d=0 high, indicating strong evidence that the null hypothesis is true. In his original treatment, Lindley (1957) showed that – under a parti
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Introduction: In this discussion from last month, computer science student and Judea Pearl collaborator Elias Barenboim expressed an attitude that hierarchical Bayesian methods might be fine in practice but that they lack theory, that Bayesians can’t succeed in toy problems. I posted a P.S. there which might not have been noticed so I will put it here: I now realize that there is some disagreement about what constitutes a “guarantee.” In one of his comments, Barenboim writes, “the assurance we have that the result must hold as long as the assumptions in the model are correct should be regarded as a guarantee.” In that sense, yes, we have guarantees! It is fundamental to Bayesian inference that the result must hold if the assumptions in the model are correct. We have lots of that in Bayesian Data Analysis (particularly in the first four chapters but implicitly elsewhere as well), and this is also covered in the classic books by Lindley, Jaynes, and others. This sort of guarantee is indeed p
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Introduction: I’ll answer the above question after first sharing some background and history on the the philosophy of Bayesian statistics, which appeared at the end of our rejoinder to the discussion to which I linked the other day: When we were beginning our statistical educations, the word ‘Bayesian’ conveyed membership in an obscure cult. Statisticians who were outside the charmed circle could ignore the Bayesian subfield, while Bayesians themselves tended to be either apologetic or brazenly defiant. These two extremes manifested themselves in ever more elaborate proposals for non-informative priors, on the one hand, and declarations of the purity of subjective probability, on the other. Much has changed in the past 30 years. ‘Bayesian’ is now often used in casual scientific parlance as a synonym for ‘rational’, the anti-Bayesians have mostly disappeared, and non-Bayesian statisticians feel the need to keep up with developments in Bayesian modelling and computation. Bayesians themselves
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Introduction: Nowadays “Bayesian” is often taken to be a synonym for rationality, and I can see how this can irritate thoughtful philosophers and statisticians alike: To start with, lots of rational thinking—even lots of rational statistical inference—does not occur within the Bayesian formalism. And, to look at it from the other direction, lots of self-proclaimed Bayesian inference hardly seems rational at all. And in what way is “subjective probability” a model for rational scientific inquiry? On the contrary, subjectivity and rationality are in many ways opposites! [emphasis added] The goal of this paper is to break the link between Bayesian modeling (good, in my opinion) and subjectivity (bad). From this perspective, the irritation of falsificationists regarding exaggerated claims of Bayesian rationality are my ally. . . . See here for the full article, to appear in the journal Rationality, Markets and Morals.
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same-blog 1 0.97668791 1259 andrew gelman stats-2012-04-11-How things sound to us, versus how they sound to others
Introduction: Hykel Hosni noticed this bit from the Lindley Prize page of the Society for Bayesan Analysis: Lindley became a great missionary for the Bayesian gospel. The atmosphere of the Bayesian revival is captured in a comment by Rivett on Lindley’s move to University College London and the premier chair of statistics in Britain: “it was as though a Jehovah’s Witness had been elected Pope.” From my perspective, this was amusing (if commonplace): a group of rationalists jocularly characterizing themselves as religious fanatics. And some of this is in response to intense opposition from outsiders (see the Background section here ). That’s my view. I’m an insider, a statistician who’s heard all jokes about religious Bayesians, from Bayesian and non-Bayesian statisticians alike. But Hosni is an outsider, and here’s how he sees the above-quoted paragraph: Research, however, is not a matter of faith but a matter of arguments, which should always be evaluated with the utmost intellec
Introduction: Updated version of my paper with Xian: The missionary zeal of many Bayesians of old has been matched, in the other direction, by an attitude among some theoreticians that Bayesian methods are absurd—not merely misguided but obviously wrong in principle. We consider several examples, beginning with Feller’s classic text on probability theory and continuing with more recent cases such as the perceived Bayesian nature of the so-called doomsday argument. We analyze in this note the intellectual background behind various misconceptions about Bayesian statistics, without aiming at a complete historical coverage of the reasons for this dismissal. I love this stuff.
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Introduction: Joel Greenhouse writes: I saw your recent paper on Feller [see here and, for a more fanciful theory, here ]. Looks like it was fun to write. I recently wrote a paper that asks an orthogonal question to yours. Why during the 1950-1960′s did Jerry Cornfield become a Bayesian? It appeared in Statistics in Medicine – “On becoming a Bayesian: Early correspondences between J. Cornfield and L. J. Savage.” In his paper, Greenhouse writes: Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. . . . Cornfield’s interest in Bayesian methods began prior to 1961 and that the clarity of his Bayesian outlook began to take shape following Birnbaum’s ASA paper on the likelihood prin- cip
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Introduction: I came across this article on the philosophy of statistics by University of Michigan economist John DiNardo. I don’t have much to say about the substance of the article because most of it is an argument against something called “Bayesian methods” that doesn’t have much in common with the Bayesian data analysis that I do. If an quantitative, empirically-minded economist at a top university doesn’t know about modern Bayesian methods, then it’s a pretty good guess that confusion holds in many other quarters as well, so I thought I’d try to clear a couple of things up. (See also here .) In the short term, I know I have some readers at the University of Michigan, so maybe a couple of you could go over to Prof. DiNardo’s office and discuss this with him? For the rest of you, please spread the word. My point here is not to claim that DiNardo should be using Bayesian methods or to claim that he’s doing anything wrong in his applied work. It’s just that he’s fighting against a bu
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Introduction: Nowadays “Bayesian” is often taken to be a synonym for rationality, and I can see how this can irritate thoughtful philosophers and statisticians alike: To start with, lots of rational thinking—even lots of rational statistical inference—does not occur within the Bayesian formalism. And, to look at it from the other direction, lots of self-proclaimed Bayesian inference hardly seems rational at all. And in what way is “subjective probability” a model for rational scientific inquiry? On the contrary, subjectivity and rationality are in many ways opposites! [emphasis added] The goal of this paper is to break the link between Bayesian modeling (good, in my opinion) and subjectivity (bad). From this perspective, the irritation of falsificationists regarding exaggerated claims of Bayesian rationality are my ally. . . . See here for the full article, to appear in the journal Rationality, Markets and Morals.
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Introduction: Antonio Ramos writes: The book with Hill has very little on longitudinal models. So do you recommended any reference to complement your book on covariance structures typical from these models, such as AR(1), Antedependence, Factor Analytic, etc? I am very much interest in BUGS code for these basic models as well as how to extend them to more complex situations. My reply: There is a book by Banerjee, Carlin, and Gelfand on Bayesian space-time models. Beyond that, I think there is good work in psychometrics on covaraince structures but I don’t know the literature.
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Introduction: As part of his continuing plan to sap etc etc., Aleks pointed me to an article by Max Miller reporting on a recommendation from Jacob Appel: Adding trace amounts of lithium to the drinking water could limit suicides. . . . Communities with higher than average amounts of lithium in their drinking water had significantly lower suicide rates than communities with lower levels. Regions of Texas with lower lithium concentrations had an average suicide rate of 14.2 per 100,000 people, whereas those areas with naturally higher lithium levels had a dramatically lower suicide rate of 8.7 per 100,000. The highest levels in Texas (150 micrograms of lithium per liter of water) are only a thousandth of the minimum pharmaceutical dose, and have no known deleterious effects. I don’t know anything about this and am offering no judgment on it; I’m just passing it on. The research studies are here and here . I am skeptical, though, about this part of the argument: We are not talking a
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