andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1528 knowledge-graph by maker-knowledge-mining
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Introduction: Stan: open-source Bayesian inference Speaker: Andrew Gelman, Columbia University Date: Thursday, October 11 2012 Time: 4:00PM to 5:00PM Location: 32-D507 Host: Polina Golland, CSAIL Contact: Polina Golland, 6172538005, polina@csail.mit.edu Stan ( mc-stan.org ) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. We discuss how Stan works and what it can do, the problems that motivated us to write Stan, current challenges, and areas of planned development, including tools for improved generality and usability, more efficient sampling algorithms, and fuller integration of model building, model checking, and model understanding in Bayesian data analysis. P.S. Here’s the talk .
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Introduction: Stan: open-source Bayesian inference Speaker: Andrew Gelman, Columbia University Date: Thursday, October 11 2012 Time: 4:00PM to 5:00PM Location: 32-D507 Host: Polina Golland, CSAIL Contact: Polina Golland, 6172538005, polina@csail.mit.edu Stan ( mc-stan.org ) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. We discuss how Stan works and what it can do, the problems that motivated us to write Stan, current challenges, and areas of planned development, including tools for improved generality and usability, more efficient sampling algorithms, and fuller integration of model building, model checking, and model understanding in Bayesian data analysis. P.S. Here’s the talk .
Introduction: Statistical Methods and Data Skepticism Data analysis today is dominated by three paradigms: null hypothesis significance testing, Bayesian inference, and exploratory data analysis. There is concern that all these methods lead to overconfidence on the part of researchers and the general public, and this concern has led to the new “data skepticism” movement. But the history of statistics is already in some sense a history of data skepticism. Concepts of bias, variance, sampling and measurement error, least-squares regression, and statistical significance can all be viewed as formalizations of data skepticism. All these methods address the concern that patterns in observed data might not generalize to the population of interest. We discuss the challenge of attaining data skepticism while avoiding data nihilism, and consider some proposed future directions. Stan Stan (mc-stan.org) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a
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Introduction: Infovis and Statistical Graphics: Different Goals, Different Looks (and here’s the article) Speaker: Andrew Gelman, Columbia University Date: Thursday, November 29 2012 Time: 4:00PM to 5:00PM Location: 32-D463 (Star Conference Room) Host: Polina Golland, CSAIL Contact: Polina Golland, 6172538005, polina@csail.mit.edu The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey’s Exploratory Data Analysis (1977) and Tufte’s books in the succeeding decades. But statistical graphics still occupies an awkward in-between position: Within statistics, exploratory and graphical methods represent a minor subfield and are not well-integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) is huge, but their purveyors and enthusiasts appear largely to be uninterested in statisti
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Introduction: Kevin Cartier writes: I’ve been happily using R for a number of years now and recently came across Stan. Looks big and powerful, so I’d like to pick an appropriate project and try it out. I wondered if you could point me to a link or document that goes into the motivation for this tool (aside from the Stan user doc)? What I’d like to understand is, at what point might you look at an emergent R project and advise, “You know, that thing you’re trying to do would be a whole lot easier/simpler/more straightforward to implement with Stan.” (or words to that effect). My reply: For my collaborators in political science, Stan has been most useful for models where the data set is not huge (e.g., we might have 10,000 data points or 50,000 data points but not 10 million) but where the model is somewhat complex (for example, a model with latent time series structure). The point is that the model has enough parameters and uncertainty that you’ll want to do full Bayes (rather than some sort
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Introduction: This blog by J. Robert Lennon on abandoned novels made me think of the more general topic of abandoned projects. I seem to recall George V. Higgins writing that he’d written and discarded 14 novels or so before publishing The Friends of Eddie Coyle. I haven’t abandoned any novels but I’ve abandoned lots of research projects (and also have started various projects that there’s no way I’ll finish). If you think about the decisions involved, it really has to be that way. You learn while you’re working on a project whether it’s worth continuing. Sometimes I’ve put in the hard work and pushed a project to completion, published the article, and then I think . . . what was the point? The modal number of citations of our articles is zero, etc.
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Introduction: When I posted this link to Dean Foster’s rants, some commenters pointed out this linked claim by famed statistician/provacateur Bjorn Lomberg: If [writes Lomborg] you reduce your child’s intake of fruits and vegetables by just 0.03 grams a day (that’s the equivalent of half a grain of rice) when you opt for more expensive organic produce, the total risk of cancer goes up, not down. Omit buying just one apple every 20 years because you have gone organic, and your child is worse off. Let’s unpack Lomborg’s claim. I don’t know anything about the science of pesticides and cancer, but can he really be so sure that the effects are so small as to be comparable to the health effects of eating “just one apple every 20 years”? I can’t believe you could estimate effects to anything like that precision. I can’t believe anyone has such a precise estimate of the health effects of pesticides, and also I can’t believe anyone has such a precise effect of the health effect of eating an app
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