andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1126 knowledge-graph by maker-knowledge-mining
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Introduction: Thurs 19 Jan 7pm at the NYC Machine Learning meetup. Stan ‘s entirely publicly funded and open-source and it has no secrets . Ask us about it and we’ll tell you everything you might want to know. P.S. And here ‘s the talk.
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same-blog 1 0.99999988 1126 andrew gelman stats-2012-01-18-Bob on Stan
Introduction: Thurs 19 Jan 7pm at the NYC Machine Learning meetup. Stan ‘s entirely publicly funded and open-source and it has no secrets . Ask us about it and we’ll tell you everything you might want to know. P.S. And here ‘s the talk.
2 0.37269253 1131 andrew gelman stats-2012-01-20-Stan: A (Bayesian) Directed Graphical Model Compiler
Introduction: Here’s Bob’s talk from the NYC machine learning meetup . And here’s Stan himself:
Introduction: My talks at Cambridge this Wed and Thurs in the department of Machine Learning . Powerpoints are here and here . Also some videos are here (but no videos of the “Nothing is Linear, Nothing is Additive” talk).
Introduction: Sander Wagner writes: I just read the post on ethical concerns in medical trials. As there seems to be a lot more pressure on private researchers i thought it might be a nice little exercise to compare p-values from privately funded medical trials with those reported from publicly funded research, to see if confirmation pressure is higher in private research (i.e. p-values are closer to the cutoff levels for significance for the privately funded research). Do you think this is a decent idea or are you sceptical? Also are you aware of any sources listing a large number of representative medical studies and their type of funding? My reply: This sounds like something worth studying. I don’t know where to get data about this sort of thing, but now that it’s been blogged, maybe someone will follow up.
5 0.15254402 1740 andrew gelman stats-2013-02-26-“Is machine learning a subset of statistics?”
Introduction: Following up on our previous post , Andrew Wilson writes: I agree we are in a really exciting time for statistics and machine learning. There has been a lot of talk lately comparing machine learning with statistics. I am curious whether you think there are many fundamental differences between the fields, or just superficial differences — different popular approximate inference methods, slightly different popular application areas, etc. Is machine learning a subset of statistics? In the paper we discuss how we think machine learning is fundamentally about pattern discovery, and ultimately, fully automating the learning and decision making process. In other words, whatever a human does when he or she uses tools to analyze data, can be written down algorithmically and automated on a computer. I am not sure if the ambitions are similar in statistics — and I don’t have any conventional statistics background, which makes it harder to tell. I think it’s an interesting discussion.
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same-blog 1 0.98359156 1126 andrew gelman stats-2012-01-18-Bob on Stan
Introduction: Thurs 19 Jan 7pm at the NYC Machine Learning meetup. Stan ‘s entirely publicly funded and open-source and it has no secrets . Ask us about it and we’ll tell you everything you might want to know. P.S. And here ‘s the talk.
2 0.90643525 1131 andrew gelman stats-2012-01-20-Stan: A (Bayesian) Directed Graphical Model Compiler
Introduction: Here’s Bob’s talk from the NYC machine learning meetup . And here’s Stan himself:
Introduction: My talks at Cambridge this Wed and Thurs in the department of Machine Learning . Powerpoints are here and here . Also some videos are here (but no videos of the “Nothing is Linear, Nothing is Additive” talk).
4 0.61485505 548 andrew gelman stats-2011-02-01-What goes around . . .
Introduction: A few weeks ago I delivered a 10-minute talk on statistical graphics that went so well, it was the best-received talk I’ve ever given. The crowd was raucous. Then some poor sap had to go on after me. He started by saying that my talk was a hard act to follow. And, indeed, the audience politely listened but did not really get involved in his presentation. Boy did I feel smug. More recently I gave a talk on Stan, at an entirely different venue. And this time the story was the exact opposite. Jim Demmel spoke first and gave a wonderful talk on optimization for linear algebra (it was an applied math conference). Then I followed, and I never really grabbed the crowd. My talk was not a disaster but it didn’t really work. This was particularly frustrating because I’m really excited about Stan and this was a group of researchers I wouldn’t usually have a chance to reach. It was the plenary session at the conference. Anyway, now I know how that guy felt from last month. My talk
5 0.61039758 712 andrew gelman stats-2011-05-14-The joys of working in the public domain
Introduction: Stan will make a total lifetime profit of $0, so we can’t be sued !
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1 0.9300878 939 andrew gelman stats-2011-10-03-DBQQ rounding for labeling charts and communicating tolerances
Introduction: This is a mini research note, not deserving of a paper, but perhaps useful to others. It reinvents what has already appeared on this blog. Let’s say we have a line chart with numbers between 152.134 and 210.823, with the mean of 183.463. How should we label the chart with about 3 tics? Perhaps 152.132, 181.4785 and 210.823? Don’t do it! Objective is to fit about 3-7 tics at the optimal level of rounding. I use the following sequence: decimal rounding : fitting integer power and single-digit decimal i , rounding to i * 10^ power (example: 100 200 300) binary having power , fitting single-digit decimal i and binary b , rounding to 2* i /(1+ b ) * 10^ power (150 200 250) (optional) quaternary having power , fitting single-digit decimal i and quaternary q (0,1,2,3) round to 4* i /(1+ q ) * 10^ power (150 175 200) quinary having power , fitting single-digit decimal i and quinary f (0,1,2,3,4) round to 5* i /(1+ f ) * 10^ power (160 180 200)
2 0.90101707 1538 andrew gelman stats-2012-10-17-Rust
Introduction: I happened to be referring to the path sampling paper today and took a look at Appendix A.2: I’m sure I could reconstruct all of this if I had to, but I certainly can’t read this sort of thing cold anymore.
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Introduction: Thurs 19 Jan 7pm at the NYC Machine Learning meetup. Stan ‘s entirely publicly funded and open-source and it has no secrets . Ask us about it and we’ll tell you everything you might want to know. P.S. And here ‘s the talk.
4 0.88435131 1515 andrew gelman stats-2012-09-29-Jost Haidt
Introduction: Research psychologist John Jost reviews the recent book, “The Righteous Mind,” by research psychologist Jonathan Haidt. Some of my thoughts on Haidt’s book are here . And here’s some of Jost’s review: Haidt’s book is creative, interesting, and provocative. . . . The book shines a new light on moral psychology and presents a bold, confrontational message. From a scientific perspective, however, I worry that his theory raises more questions than it answers. Why do some individuals feel that it is morally good (or necessary) to obey authority, favor the ingroup, and maintain purity, whereas others are skeptical? (Perhaps parenting style is relevant after all.) Why do some people think that it is morally acceptable to judge or even mistreat others such as gay or lesbian couples or, only a generation ago, interracial couples because they dislike or feel disgusted by them, whereas others do not? Why does the present generation “care about violence toward many more classes of victims
5 0.88175321 845 andrew gelman stats-2011-08-08-How adoption speed affects the abandonment of cultural tastes
Introduction: Interesting article by Jonah Berger and Gael Le Mens: Products, styles, and social movements often catch on and become popular, but little is known about why such identity-relevant cultural tastes and practices die out. We demonstrate that the velocity of adoption may affect abandonment: Analysis of over 100 years of data on first-name adoption in both France and the United States illustrates that cultural tastes that have been adopted quickly die faster (i.e., are less likely to persist). Mirroring this aggregate pattern, at the individual level, expecting parents are more hesitant to adopt names that recently experienced sharper increases in adoption. Further analysis indicate that these effects are driven by concerns about symbolic value: Fads are perceived negatively, so people avoid identity-relevant items with sharply increasing popularity because they believe that they will be short lived. Ancillary analyses also indicate that, in contrast to conventional wisdom, identity-r
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