andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1958 knowledge-graph by maker-knowledge-mining
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Introduction: Aleks pointed me to some posts on Gary Rubenstein’s blog, where I found this disheartening description of the teaching at a local high school. None of this is a surprise, but Rubenstein writes it vividly, and it’s interesting to see how bad things can be at school, on a day-by-day basis.
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same-blog 1 1.0 1958 andrew gelman stats-2013-07-27-Teaching is hard
Introduction: Aleks pointed me to some posts on Gary Rubenstein’s blog, where I found this disheartening description of the teaching at a local high school. None of this is a surprise, but Rubenstein writes it vividly, and it’s interesting to see how bad things can be at school, on a day-by-day basis.
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Introduction: Aleks pointed me to this .
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Introduction: Aleks sent me this (I’m not sure from where):
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Introduction: I have no idea what this and this are, but Aleks passed these on, and maybe some of you will find them interesting.
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Introduction: Aleks pointed me to this interesting article by Sarah Slobin. I don’t have any good way of summarizing it, so you’ll just have to follow the link and read it.
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Introduction: Aleks pointed me to some posts on Gary Rubenstein’s blog, where I found this disheartening description of the teaching at a local high school. None of this is a surprise, but Rubenstein writes it vividly, and it’s interesting to see how bad things can be at school, on a day-by-day basis.
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Introduction: I have no idea what this and this are, but Aleks passed these on, and maybe some of you will find them interesting.
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Introduction: Aleks pointed me to this .
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Introduction: Aleks points us to this idea of labeling for news.
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Introduction: Aleks sent me this (I’m not sure from where):
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Introduction: Speakers: Cyrus Samii, PhD candidate, Department of Political Science, Columbia University: “Peacebuilding Policies as Quasi-Experiments: Some Examples” Macartan Humphreys, Associate Professor, Department of Political Science, Columbia University: “Sampling in developing countries: Five challenges from the field” Friday 22 Oct, 3-5pm in the Playroom (707 International Affairs Building). Open to all.
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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: Aleks pointed me to some posts on Gary Rubenstein’s blog, where I found this disheartening description of the teaching at a local high school. None of this is a surprise, but Rubenstein writes it vividly, and it’s interesting to see how bad things can be at school, on a day-by-day basis.
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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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Introduction: David Hogg pointed me to this news article by Angela Saini: It’s not often that the quiet world of mathematics is rocked by a murder case. But last summer saw a trial that sent academics into a tailspin, and has since swollen into a fevered clash between science and the law. At its heart, this is a story about chance. And it begins with a convicted killer, “T”, who took his case to the court of appeal in 2010. Among the evidence against him was a shoeprint from a pair of Nike trainers, which seemed to match a pair found at his home. While appeals often unmask shaky evidence, this was different. This time, a mathematical formula was thrown out of court. The footwear expert made what the judge believed were poor calculations about the likelihood of the match, compounded by a bad explanation of how he reached his opinion. The conviction was quashed. . . . “The impact will be quite shattering,” says Professor Norman Fenton, a mathematician at Queen Mary, University of London.
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