andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-699 knowledge-graph by maker-knowledge-mining
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Introduction: I’ve heard from various sources that when you give a talk in an econ dept that they eat you alive: typically the audience showers you with questions and you are lucky to get past the second slide in your presentation. So far, though, I’ve given seminar talks in three economics departments–George Mason University a few years ago, Sciences Po last year, and Hunter College yesterday–and all three times the audiences have been completely normal. They did not interrupt unduly and they asked a bunch of good questions at the end. n=3, sure. But still.
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1 I’ve heard from various sources that when you give a talk in an econ dept that they eat you alive: typically the audience showers you with questions and you are lucky to get past the second slide in your presentation. [sent-1, score-2.097]
2 So far, though, I’ve given seminar talks in three economics departments–George Mason University a few years ago, Sciences Po last year, and Hunter College yesterday–and all three times the audiences have been completely normal. [sent-2, score-1.367]
3 They did not interrupt unduly and they asked a bunch of good questions at the end. [sent-3, score-0.933]
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Introduction: I’ve heard from various sources that when you give a talk in an econ dept that they eat you alive: typically the audience showers you with questions and you are lucky to get past the second slide in your presentation. So far, though, I’ve given seminar talks in three economics departments–George Mason University a few years ago, Sciences Po last year, and Hunter College yesterday–and all three times the audiences have been completely normal. They did not interrupt unduly and they asked a bunch of good questions at the end. n=3, sure. But still.
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Introduction: I’ve heard all sorts of scare stories of what it’s like to speak in an academic economics seminar: they’re rude, they interrupt constantly, they don’t let you get through three slides in an hour, etc. But whenever I’ve actually spoke in an economics department, the people have been polite and well-behaved, really it’s been like any other seminar. I mentioned this to some people awhile ago and they said that the nasty-economist thing only happens in the top departments. I’d spoken at Columbia (which, if not at the very top, is still respectable), but that was in the political economy seminar and I’m a political scientist, so maybe they were nice to me because I’m local. And the other econ departments where I’d spoken were in Europe (maybe they’re nicer there) or at non-elite institutions in the U.S. So I called my friend at Harvard econ, told him my story, and asked if I could speak there. He duly booked me for the Harvard-MIT econometrics seminar. I spoke at the seminar,
Introduction: From the classic Box, Hunter, and Hunter book. The point of the saying is pretty clear, I think: There are things you learn from perturbing a system that you’ll never find out from any amount of passive observation. This is not always true–sometimes “nature” does the experiment for you–but I think it represents an important insight. I’m currently writing (yet another) review article on causal inference and am planning use this quote. P.S. I find it helpful to write these reviews for a similar reason that I like to blog on certain topics over and over, each time going a bit further (I hope) than the time before. Beyond the benefit of communicating my recommendations to new audiences, writing these sorts of reviews gives me an excuse to explore my thoughts in more rigor. P.P.S. In the original version of this blog entry, I correctly attributed the quote to Box but I incorrectly remembered it as “No understanding without manipulation.” Karl Broman (see comment below) gave me
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Introduction: I just gave a talk in Milan. Actually I was sitting at my desk, it was a g+ hangout which was a bit more convenient for me. The audience was a bunch of astronomers so I figured they could handle a satellite link. . . . Anyway, the talk didn’t go so well. Two reasons: first, it’s just hard to get the connection with the audience without being able to see their faces. Next time I think I’ll try to get several people in the audience to open up their laptops and connect to the hangout, so that I can see a mosaic of faces instead of just a single image from the front of the room. The second problem with the talk was the topic. I asked the people who invited me to choose a topic, and they picked Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up? But I don’t think this was right for this audience. I think that it would’ve been better to give them the Stan talk or the little data talk or the statistic
Introduction: About 12 years ago Greg Wawro, Sy Spilerman, and I started a M.A. program here in Quantitative Methods in Social Sciences, jointly between the departments of history, economics, political science, sociology, psychology, and statistics. We created a bunch of new features for the program, including an interdisciplinary course based on this book . And here’s their new logo: Don’t blame me for the pie-chart motif! Seriously, though, the program is great. I’m proud to have gotten it started, and I’m impressed by the progress that Chris Weiss and others have made in expanding the program during the past decade.
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Introduction: I’ve heard from various sources that when you give a talk in an econ dept that they eat you alive: typically the audience showers you with questions and you are lucky to get past the second slide in your presentation. So far, though, I’ve given seminar talks in three economics departments–George Mason University a few years ago, Sciences Po last year, and Hunter College yesterday–and all three times the audiences have been completely normal. They did not interrupt unduly and they asked a bunch of good questions at the end. n=3, sure. But still.
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Introduction: I have this great talk on the above topic but nowhere to give it. Here’s the story. Several months ago, I was invited to speak at IEEE VisWeek. It sounded like a great opportunity. The organizer told me that there were typically about 700 people in the audience, and these are people in the visualization community whom I’d like to reach but normally wouldn’t have the opportunity to encounter. It sounded great, but I didn’t want to fly most of the way across the country by myself, so I offered to give the talk by videolink. I was surprised to get a No response: I’d think that a visualization conference, of all things, would welcome a video talk. In the meantime, though, I’d thought a lot about what I’d talk about and had started preparing something. Once I found out I wouldn’t be giving the talk, I channeled the efforts into an article which, with the collaboration of Antony Unwin, was completed about a month ago. It would take very little effort to adapt this graph-laden a
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Introduction: At the City University of New York Graduate Center, 365 Fifth Avenue (between 34th and 35th street), room 6002. The topic: causality and statistical learning . Announcement is here (scroll down). It says that if you would like to attend any event, please respond by emailing datamining@gc.cuny.edu I’m also giving a shorter talk on the same topic in the Sustainable Development Seminar Series 4pm Monday 29 Apr in room 407 International Affairs Bldg (at 118th St. and Amsterdam Ave.). It’s just a coincidence that I’m giving the same talk twice. I was asked at different times to speak for these groups. When someone asks me to speak, I let them pick from recent talks on the list .
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Introduction: Michael Betancourt will be speaking at Google and at the University of California, Berkeley. The Google talk is closed to outsiders (but if you work at Google, you should go!); the Berkeley talk is open to all: Friday March 22, 12:10 pm, Evans Hall 1011. Title of talk: Stan : Practical Bayesian Inference with Hamiltonian Monte Carlo Abstract: Practical implementations of Bayesian inference are often limited to approximation methods that only slowly explore the posterior distribution. By taking advantage of the curvature of the posterior, however, Hamiltonian Monte Carlo (HMC) efficiently explores even the most highly contorted distributions. In this talk I will review the foundations of and recent developments within HMC, concluding with a discussion of Stan, a powerful inference engine that utilizes HMC, automatic differentiation, and adaptive methods to minimize user input. This is cool stuff. And he’ll be showing the whirlpool movie!
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Introduction: Michael Betancourt will be speaking at UCLA: The location for refreshment is in room 51-254 CHS at 3:00 PM. The place for the seminar is at CHS 33-105A at 3:30pm – 4:30pm, Wed 6 Mar. ["CHS" stands for Center for Health Sciences, the building of the UCLA schools of medicine and public health. Here's a map with directions .] Title of talk: Stan : Practical Bayesian Inference with Hamiltonian Monte Carlo Abstract: Practical implementations of Bayesian inference are often limited to approximation methods that only slowly explore the posterior distribution. By taking advantage of the curvature of the posterior, however, Hamiltonian Monte Carlo (HMC) efficiently explores even the most highly contorted distributions. In this talk I will review the foundations of and recent developments within HMC, concluding with a discussion of Stan, a powerful inference engine that utilizes HMC, automatic differentiation, and adaptive methods to minimize user input. This is cool stuff.
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