andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2252 knowledge-graph by maker-knowledge-mining
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Introduction: À 11h15 au Centre de Mathématiques Appliquées: Peut-on utiliser les méthodes bayésiennes pour résoudre la crise des résultats de la recherche statistiquement significatifs que ne tiennent pas? It’s the usual story: the audience will be technical but with a varying mix of interests, and so what they most wanted to hear was something general and nontechnical. But this work does have connections to more involved research on interactions and weakly informative priors.
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same-blog 1 1.0 2252 andrew gelman stats-2014-03-17-Ma conférence demain (mardi) à l’École Polytechnique
Introduction: À 11h15 au Centre de Mathématiques Appliquées: Peut-on utiliser les méthodes bayésiennes pour résoudre la crise des résultats de la recherche statistiquement significatifs que ne tiennent pas? It’s the usual story: the audience will be technical but with a varying mix of interests, and so what they most wanted to hear was something general and nontechnical. But this work does have connections to more involved research on interactions and weakly informative priors.
Introduction: As I wrote a couple years ago: Even though statistical analysis has demonstrated that presidential elections are predictable given economic conditions and previous votes in the states . . . it certainly doesn’t mean that every election can be accurately predicted ahead of time. Presidential general election campaigns have several distinct features that distinguish them from most other elections: 1. Two major candidates; 2. The candidates clearly differ in their political ideologies and in their positions on economic issues; 3. The two sides have roughly equal financial and organizational resources; 4. The current election is the latest in a long series of similar contests (every four years); 5. A long campaign, giving candidates a long time to present their case and giving voters a long time to make up their minds. Other elections look different. . . . Or, as I said in reference to the current NYC mayoral election: Et selon Andrew Gelman, expert de l’universi
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Introduction: Modélisation hiérarchique, pooling partiel et l’interrogation de bases de données virtuelles P.S. Here are the slides . I only got through a few of them. I have to remember that when I speak in another language, I go much slower.
Introduction: Deborah Mayo sent me this quote from Jim Berger: Too often I see people pretending to be subjectivists, and then using “weakly informative” priors that the objective Bayesian community knows are terrible and will give ridiculous answers; subjectivism is then being used as a shield to hide ignorance. . . . In my own more provocative moments, I claim that the only true subjectivists are the objective Bayesians, because they refuse to use subjectivism as a shield against criticism of sloppy pseudo-Bayesian practice. This caught my attention because I’ve become more and more convinced that weakly informative priors are the right way to go in many different situations. I don’t think Berger was talking about me , though, as the above quote came from a publication in 2006, at which time I’d only started writing about weakly informative priors. Going back to Berger’s article , I see that his “weakly informative priors” remark was aimed at this article by Anthony O’Hagan, who w
5 0.14366357 1092 andrew gelman stats-2011-12-29-More by Berger and me on weakly informative priors
Introduction: A couple days ago we discussed some remarks by Tony O’Hagan and Jim Berger on weakly informative priors. Jim followed up on Deborah Mayo’s blog with this: Objective Bayesian priors are often improper (i.e., have infinite total mass), but this is not a problem when they are developed correctly. But not every improper prior is satisfactory. For instance, the constant prior is known to be unsatisfactory in many situations. The ‘solution’ pseudo-Bayesians often use is to choose a constant prior over a large but bounded set (a ‘weakly informative’ prior), saying it is now proper and so all is well. This is not true; if the constant prior on the whole parameter space is bad, so will be the constant prior over the bounded set. The problem is, in part, that some people confuse proper priors with subjective priors and, having learned that true subjective priors are fine, incorrectly presume that weakly informative proper priors are fine. I have a few reactions to this: 1. I agree
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same-blog 1 0.97777969 2252 andrew gelman stats-2014-03-17-Ma conférence demain (mardi) à l’École Polytechnique
Introduction: À 11h15 au Centre de Mathématiques Appliquées: Peut-on utiliser les méthodes bayésiennes pour résoudre la crise des résultats de la recherche statistiquement significatifs que ne tiennent pas? It’s the usual story: the audience will be technical but with a varying mix of interests, and so what they most wanted to hear was something general and nontechnical. But this work does have connections to more involved research on interactions and weakly informative priors.
2 0.68263435 468 andrew gelman stats-2010-12-15-Weakly informative priors and imprecise probabilities
Introduction: Giorgio Corani writes: Your work on weakly informative priors is close to some research I [Corani] did (together with Prof. Zaffalon) in the last years using the so-called imprecise probabilities. The idea is to work with a set of priors (containing even very different priors); to update them via Bayes’ rule and then compute a set of posteriors. The set of priors is convex and the priors are Dirichlet (thus, conjugate to the likelihood); this allows to compute the set of posteriors exactly and efficiently. I [Corani] have used this approach for classification, extending naive Bayes and TAN to imprecise probabilities. Classifiers based on imprecise probabilities return more classes when they find that the most probable class is prior-dependent, i.e., if picking different priors in the convex set leads to identify different classes as the most probable one. Instead of returning a single (unreliable) prior-dependent class, credal classifiers in this case preserve reliability by
Introduction: Deborah Mayo sent me this quote from Jim Berger: Too often I see people pretending to be subjectivists, and then using “weakly informative” priors that the objective Bayesian community knows are terrible and will give ridiculous answers; subjectivism is then being used as a shield to hide ignorance. . . . In my own more provocative moments, I claim that the only true subjectivists are the objective Bayesians, because they refuse to use subjectivism as a shield against criticism of sloppy pseudo-Bayesian practice. This caught my attention because I’ve become more and more convinced that weakly informative priors are the right way to go in many different situations. I don’t think Berger was talking about me , though, as the above quote came from a publication in 2006, at which time I’d only started writing about weakly informative priors. Going back to Berger’s article , I see that his “weakly informative priors” remark was aimed at this article by Anthony O’Hagan, who w
4 0.58588684 1209 andrew gelman stats-2012-03-12-As a Bayesian I want scientists to report their data non-Bayesianly
Introduction: Philipp Doebler writes: I was quite happy that recently you shared some thoughts of yours and others on meta-analysis. I especially liked the slides by Chris Schmid that you linked from your blog. A large portion of my work deals with meta-analysis and I am also fond of using Bayesian methods (actually two of the projects I am working on are very Bayesian), though I can not say I have opinions with respect to the underlying philosophy. I would say though, that I do share your view that there are good reasons to use informative priors. The reason I am writing to you is that this leads to the following dilemma, which is puzzling me. Say a number of scientists conduct similar studies over the years and all of them did this in a Bayesian fashion. If each of the groups used informative priors based on the research of existing groups the priors could become more and more informative over the years, since more and more is known over the subject. At least in smallish studies these p
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Introduction: À 11h15 au Centre de Mathématiques Appliquées: Peut-on utiliser les méthodes bayésiennes pour résoudre la crise des résultats de la recherche statistiquement significatifs que ne tiennent pas? It’s the usual story: the audience will be technical but with a varying mix of interests, and so what they most wanted to hear was something general and nontechnical. But this work does have connections to more involved research on interactions and weakly informative priors.
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Introduction: I received the following (unsolicited) email today: Hello Andrew, I’m interested in whether you are accepting guest article submissions for your site Statistical Modeling, Causal Inference, and Social Science? I’m the owner of the recently created nonprofit site OnlineEngineeringDegree.org and am interested in writing / submitting an article for your consideration to be published on your site. Is that something you’d be willing to consider, and if so, what specs in terms of topics or length requirements would you be looking for? Thanks you for your time, and if you have any questions or are interested, I’d appreciate you letting me know. Sincerely, Samantha Rhodes Huh? P.S. My vote for most obnoxious spam remains this one , which does its best to dilute whatever remains of the reputation of Wolfram Research. Or maybe that particular bit of spam was written by a particularly awesome cellular automaton that Wolfram discovered? I guess in the world of big-time software
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Introduction: People sometimes email asking if a solution set is available for the exercises in ARM. The answer, unfortunately, is no. Many years ago, I wrote up 50 solutions for BDA and it was a lot of work–really, it was like writing a small book in itself. The trouble is that, once I started writing them up, I wanted to do it right, to set a good example. That’s a lot more effort than simply scrawling down some quick answers.
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Introduction: Steve Ziliak points me to this article by the always-excellent Carl Bialik, slamming hypothesis tests. I only wish Carl had talked with me before so hastily posting, though! I would’ve argued with some of the things in the article. In particular, he writes: Reese and Brad Carlin . . . suggest that Bayesian statistics are a better alternative, because they tackle the probability that the hypothesis is true head-on, and incorporate prior knowledge about the variables involved. Brad Carlin does great work in theory, methods, and applications, and I like the bit about the prior knowledge (although I might prefer the more general phrase “additional information”), but I hate that quote! My quick response is that the hypothesis of zero effect is almost never true! The problem with the significance testing framework–Bayesian or otherwise–is in the obsession with the possibility of an exact zero effect. The real concern is not with zero, it’s with claiming a positive effect whe
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