andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-903 knowledge-graph by maker-knowledge-mining
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Introduction: I hate to announce this one because it’s directly competing with us, but it actually looks pretty good! If I were getting my Ph.D. right now, I’d definitely apply . . . David Dunson announces: There will be several postdoctoral fellowships available at Duke to work with me [Dunson] & others on research related to foundations of nonparametric Bayes in high-dimensional settings, with a particular focus on showing theoretical properties and developing new models and computational approaches in machine learning applications & genomics. Send applications to Ellen Currin, Department of Electrical and Computer Engineering, ecurrin@ee.duke.edu
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2 Send applications to Ellen Currin, Department of Electrical and Computer Engineering, ecurrin@ee. [sent-8, score-0.246]
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same-blog 1 1.0 903 andrew gelman stats-2011-09-13-Duke postdoctoral fellowships in nonparametric Bayes & high-dimensional data
Introduction: I hate to announce this one because it’s directly competing with us, but it actually looks pretty good! If I were getting my Ph.D. right now, I’d definitely apply . . . David Dunson announces: There will be several postdoctoral fellowships available at Duke to work with me [Dunson] & others on research related to foundations of nonparametric Bayes in high-dimensional settings, with a particular focus on showing theoretical properties and developing new models and computational approaches in machine learning applications & genomics. Send applications to Ellen Currin, Department of Electrical and Computer Engineering, ecurrin@ee.duke.edu
2 0.18325794 1858 andrew gelman stats-2013-05-15-Reputations changeable, situations tolerable
Introduction: David Kessler, Peter Hoff, and David Dunson write : Marginally specified priors for nonparametric Bayesian estimation Prior specification for nonparametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. Realistically, a statistician is unlikely to have informed opinions about all aspects of such a parameter, but may have real information about functionals of the parameter, such the population mean or variance. This article proposes a new framework for nonparametric Bayes inference in which the prior distribution for a possibly infinite-dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a nonparametric conditional prior for the parameter given the functionals. Such priors can be easily constructed from standard nonparametric prior distributions in common use, and inherit the large support of the standard priors upon which they are based. Ad
3 0.15674463 1454 andrew gelman stats-2012-08-11-Weakly informative priors for Bayesian nonparametric models?
Introduction: Nathaniel Egwu writes: I am a PhD student working on machine learning using artificial neural networks . . . Do you have some recent publications related to how one can construct priors depending on the type of input data available for training? I intend to construct a prior distribution for a given trade-off parameter of my non model obtained through training a neural network. At this stage, my argument is due to the fact that Bayesian nonparameteric estimation offers some insight on how to proceed on this problem. As I’ve been writing here for awhile, I’ve been interested in weakly informative priors. But I have little experience with nonparametric models. Perhaps Aki Vehtari or David Dunson or some other expert on these models can discuss how to set them up with weakly informative priors? This sounds like it could be important to me.
4 0.1301728 1482 andrew gelman stats-2012-09-04-Model checking and model understanding in machine learning
Introduction: Last month I wrote : Computer scientists are often brilliant but they can be unfamiliar with what is done in the worlds of data collection and analysis. This goes the other way too: statisticians such as myself can look pretty awkward, reinventing (or failing to reinvent) various wheels when we write computer programs or, even worse, try to design software.Andrew MacNamara writes: Andrew MacNamara followed up with some thoughts: I [MacNamara] had some basic statistics training through my MBA program, after having completed an undergrad degree in computer science. Since then I’ve been very interested in learning more about statistical techniques, including things like GLM and censored data analyses as well as machine learning topics like neural nets, SVMs, etc. I began following your blog after some research into Bayesian analysis topics and I am trying to dig deeper on that side of things. One thing I have noticed is that there seems to be a distinction between data analysi
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Introduction: David Blei writes: I have two postdoc openings for basic research in probabilistic modeling . The thrusts are (a) scalable inference and (b) model checking. We will be developing new methods and implementing them in probabilistic programming systems. I am open to applicants interested in many kinds of applications and from any field. “Scalable inference” means black-box VB and related ideas, and “probabilistic programming systems” means Stan! (You might be familiar with Stan as an implementation of Nuts for posterior sampling, but Stan is also an efficient program for computing probability densities and their gradients, and as such is an ideal platform for developing scalable implementations of variational inference and related algorithms.) And you know I like model checking. Here’s the full ad: ===== POSTDOC POSITIONS IN PROBABILISTIC MODELING ===== We expect to have two postdoctoral positions available for January 2014 (or later). These positions are in D
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same-blog 1 0.97829312 903 andrew gelman stats-2011-09-13-Duke postdoctoral fellowships in nonparametric Bayes & high-dimensional data
Introduction: I hate to announce this one because it’s directly competing with us, but it actually looks pretty good! If I were getting my Ph.D. right now, I’d definitely apply . . . David Dunson announces: There will be several postdoctoral fellowships available at Duke to work with me [Dunson] & others on research related to foundations of nonparametric Bayes in high-dimensional settings, with a particular focus on showing theoretical properties and developing new models and computational approaches in machine learning applications & genomics. Send applications to Ellen Currin, Department of Electrical and Computer Engineering, ecurrin@ee.duke.edu
Introduction: One thing we do here at the Applied Statistics Center is hold mini-conferences. The next one looks really cool. It’s organized by Sharad Goel and Jake Hofman (Microsoft Research, formerly at Yahoo Research), David Park (Columbia University), and Sergei Vassilvitskii (Google). As with our other conferences, one of our goals is to mix the academic and nonacademic research communities. Here’s the website for the workshop, and here’s the announcement from the organizers: With an explosion of data on every aspect of our everyday existence — from what we buy, to where we travel, to who we know — we are able to observe human behavior with granularity largely thought impossible just a decade ago. The growth of such online activity has further facilitated the design of web-based experiments, enhancing both the scale and efficiency of traditional methods. Together these advances have created an unprecedented opportunity to address longstanding questions in the social sciences, rang
3 0.71896774 1992 andrew gelman stats-2013-08-21-Workshop for Women in Machine Learning
Introduction: This might interest some of you: CALL FOR ABSTRACTS Workshop for Women in Machine Learning Co-located with NIPS 2013, Lake Tahoe, Nevada, USA December 5, 2013 http://www.wimlworkshop.org Deadline for abstract submissions: September 16, 2013 WORKSHOP DESCRIPTION The Workshop for Women in Machine Learning is a day-long event taking place on the first day of NIPS. The workshop aims to showcase the research of women in machine learning and to strengthen their community. The event brings together female faculty, graduate students, and research scientists for an opportunity to connect, exchange ideas, and learn from each other. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. While all presenters will be female, all genders are invited to attend. Scholarships will be provided to female students and postdoctoral attendees with accepted abstracts to partially offset travel costs. Workshop
Introduction: David Ebert sends this along: Purdue University School of ECE Faculty Position in Human-Centered Computing The School of Electrical and Computer Engineering at Purdue University invites applications for a faculty position at any level in human-centered computing, including but not limited to visualization, visual analytics, human-computer interaction (HCI), imaging, and graphics. . . . Applications should consist of a cover letter, a CV, research and teaching statements, names and contact information for at least three references, and URLs for three to five online papers. . . . We will consider applications through March 2013. It’s great to see this sort of thing. P.S. Amusingly enough, the Purdue Visualization and Analytics Center has an ugly, bureaucratic, text-heavy webpage . Not that I’m one to talk, the Columbia stat dept has an ugly webpage too (although I think we’ll be switching soon to something better).
5 0.71635371 1961 andrew gelman stats-2013-07-29-Postdocs in probabilistic modeling! With David Blei! And Stan!
Introduction: David Blei writes: I have two postdoc openings for basic research in probabilistic modeling . The thrusts are (a) scalable inference and (b) model checking. We will be developing new methods and implementing them in probabilistic programming systems. I am open to applicants interested in many kinds of applications and from any field. “Scalable inference” means black-box VB and related ideas, and “probabilistic programming systems” means Stan! (You might be familiar with Stan as an implementation of Nuts for posterior sampling, but Stan is also an efficient program for computing probability densities and their gradients, and as such is an ideal platform for developing scalable implementations of variational inference and related algorithms.) And you know I like model checking. Here’s the full ad: ===== POSTDOC POSITIONS IN PROBABILISTIC MODELING ===== We expect to have two postdoctoral positions available for January 2014 (or later). These positions are in D
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same-blog 1 0.92336261 903 andrew gelman stats-2011-09-13-Duke postdoctoral fellowships in nonparametric Bayes & high-dimensional data
Introduction: I hate to announce this one because it’s directly competing with us, but it actually looks pretty good! If I were getting my Ph.D. right now, I’d definitely apply . . . David Dunson announces: There will be several postdoctoral fellowships available at Duke to work with me [Dunson] & others on research related to foundations of nonparametric Bayes in high-dimensional settings, with a particular focus on showing theoretical properties and developing new models and computational approaches in machine learning applications & genomics. Send applications to Ellen Currin, Department of Electrical and Computer Engineering, ecurrin@ee.duke.edu
2 0.86438268 813 andrew gelman stats-2011-07-21-Scrabble!
Introduction: AT writes : Sitting on my [AT's] to-do list for a while now has been an exploration of Scrabble from an experimental design point of view; how to better design a tournament to make the variance as small as possible while still preserving the appearance of the home game to its players. . . . I’m proud (relieved?) to say that I’ve finally finished the first draft of this work for two-player head-to-head games, with a duplication method that ensures that if the game were repeated, each player would receive tiles from the reserve in the same sequence: think of the tiles being laid out in order (but unseen to the players), so that one player draws from the front and the other draws from the back. . . . One goal of this was to figure out how much of the variance in score comes from the tile order and how much comes from the board, given that a tile order would be expected. It turns out to be about half-bag, half-board . . . Some other findings from the simulations: The blank
3 0.83943927 1961 andrew gelman stats-2013-07-29-Postdocs in probabilistic modeling! With David Blei! And Stan!
Introduction: David Blei writes: I have two postdoc openings for basic research in probabilistic modeling . The thrusts are (a) scalable inference and (b) model checking. We will be developing new methods and implementing them in probabilistic programming systems. I am open to applicants interested in many kinds of applications and from any field. “Scalable inference” means black-box VB and related ideas, and “probabilistic programming systems” means Stan! (You might be familiar with Stan as an implementation of Nuts for posterior sampling, but Stan is also an efficient program for computing probability densities and their gradients, and as such is an ideal platform for developing scalable implementations of variational inference and related algorithms.) And you know I like model checking. Here’s the full ad: ===== POSTDOC POSITIONS IN PROBABILISTIC MODELING ===== We expect to have two postdoctoral positions available for January 2014 (or later). These positions are in D
4 0.83411515 1332 andrew gelman stats-2012-05-20-Problemen met het boek
Introduction: Regarding the so-called Dutch Book argument for Bayesian inference (the idea that, if your inferences do not correspond to a Bayesian posterior distribution, you can be forced to make incoherent bets and ultimately become a money pump), I wrote: I have never found this argument appealing, because a bet is a game not a decision. A bet requires 2 players, and one player has to offer the bets. I do agree that in some bounded settings (for example, betting on win place show in a horse race), I’d want my bets to be coherent; if they are incoherent (e.g., if my bets correspond to P(A|B)*P(B) not being equal to P(A,B)), then I should be able to do better by examining the incoherence. But in an “open system” (to borrow some physics jargon), I don’t think coherence is possible. There is always new information coming in, and there is always additional prior information in reserve that hasn’t entered the model.
5 0.83083934 633 andrew gelman stats-2011-03-28-“The New Tyranny: Carbon Monoxide Detectors?”
Introduction: This story reminds me that, when I was in grad school, the state of Massachusetts instituted a seat-belt law which became a big controversy. A local talk show host made it his pet project to shoot down the law, and he succeeded! There was a ballot initiative and the voters repealed the seat belt law. A few years later the law returned (it was somehow tied in with Federal highway funding, I think, the same way they managed to get all the states to up the drinking age to 21), and, oddly enough, nobody seemed to care the second time around. It’s funny how something can be a big political issue one year and nothing the next. I have no deep insights on the matter, but it’s worth remembering that these sorts of panics are nothing new. Recall E.S. Turner’s classic book, Roads to Ruin. I think there’s a research project in here, to understand what gets an issue to be a big deal and how it is that some controversies just fade away.
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