andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1112 knowledge-graph by maker-knowledge-mining
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Introduction: From Allen Downey .
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same-blog 1 1.0 1112 andrew gelman stats-2012-01-11-A blog full of examples for your statistics class
Introduction: From Allen Downey .
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Introduction: A great new blog-class by Shawn Allen at Data Visualization , assembling all the good stuff in one place.
3 0.20783204 1948 andrew gelman stats-2013-07-21-Bayes related
Introduction: Dave Decker writes: I’ve seen some Bayes related things recently that might make for interesting fodder on your blog. There are two books, teaching Bayesian analysis from a programming perspective. And also a “web application for data analysis using powerful Bayesian statistical methods.” I took a look. The first book is Think Bayes: Bayesian Statistics Made Simple, by Allen B. Downey . It’s super readable and, amazingly, has approximately zero overlap with Bayesian Data Analysis. Downey discusses lots of little problems in a conversational way. In some ways it’s like an old-style math stat textbook (although with a programming rather than mathematical flavor) in that the examples are designed for simplicity rather than realism. I like it! Our book already exists; it’s good to have something else for people to read, coming from an entirely different perspective. The second book is Probabilistic Programming and Bayesian Methods for Hackers , by Cameron Davidson-P
Introduction: In response to my remarks on his online book, Think Bayes, Allen Downey wrote: I [Downey] have a question about one of your comments: My [Gelman's] main criticism with both books is that they talk a lot about inference but not so much about model building or model checking (recall the three steps of Bayesian data analysis). I think it’s ok for an introductory book to focus on inference, which of course is central to the data-analytic process—but I’d like them to at least mention that Bayesian ideas arise in model building and model checking as well. This sounds like something I agree with, and one of the things I tried to do in the book is to put modeling decisions front and center. But the word “modeling” is used in lots of ways, so I want to see if we are talking about the same thing. For example, in many chapters, I start with a simple model of the scenario, do some analysis, then check whether the model is good enough, and iterate. Here’s the discussion of modeling
5 0.10077614 2206 andrew gelman stats-2014-02-10-On deck this week
Introduction: This blog has roughly a month’s worth of items waiting to be posted. I post about once a day, sometimes rescheduling posts to make room for something topical. Anyway, it struck me that I know what’s coming up, but you don’t. So, here’s what we have for you during the next few days: Mon: More on US health care overkill Tues: My talks in Bristol this Wed and London this Thurs Wed: How to think about “identifiability” in Bayesian inference? Thurs: Stopping rules and Bayesian analysis Fri: The popularity of certain baby names is falling off the clifffffffffffff Plus anything our cobloggers might choose to post during these days. And, if Woody Allen or Ed Wegman or anyone else newsworthy asks us to publish an op-ed for them, we’ll consider it. Enjoy.
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Introduction: From Allen Downey .
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Introduction: A great new blog-class by Shawn Allen at Data Visualization , assembling all the good stuff in one place.
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Introduction: (Click for bigger image.) The above is Aki’s decomposition of the birthdays data (the number of babies born each day in the United States, from 1968 through 1988) using a Gaussian process model, as described in more detail in our book .
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Introduction: Good stuff.
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Introduction: Good stuff , as always, from Laura Wattenberg.
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Introduction: From Allen Downey .
2 0.43235305 818 andrew gelman stats-2011-07-23-Parallel JAGS RNGs
Introduction: As a matter of convention, we usually run 3 or 4 chains in JAGS. By default, this gives rise to chains that draw samples from 3 or 4 distinct pseudorandom number generators. I didn’t go and check whether it does things 111,222,333 or 123,123,123, but in any event the “parallel chains” in JAGS are samples drawn from distinct RNGs computed on a single processor core. But we all have multiple cores now, or we’re computing on a cluster or the cloud! So the behavior we’d like from rjags is to use the foreach package with each JAGS chain using a parallel-safe RNG. The default behavior with n.chain=1 will be that each parallel instance will use .RNG.name[1] , the Wichmann-Hill RNG. JAGS 2.2.0 includes a new lecuyer module (along with the glm module, which everyone should probably always use, and doesn’t have many undocumented tricks that I know of). But lecuyer is completely undocumented! I tried .RNG.name="lecuyer::Lecuyer" , .RNG.name="lecuyer::lecuyer" , and .RNG.name=
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Introduction: I was recently speaking with a member of the U.S. House of Representatives, a Californian in a tight race this year. I mentioned the fivethirtyeight.com prediction for him, and he said “fivethirtyeight.com? What’s that?”
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Introduction: Peng Yu writes: On page 180 of BDA2, deviance is defined as D(y,\theta)=-2log p(y|\theta). However, according to GLM 2/e by McCullagh and Nelder, deviance is the different of the log-likelihood of the full model and the base model (times 2) (see the equation on the wiki webpage). The english word ‘deviance’ implies the difference from a standard (in this case, the base model). I’m wondering what the rationale for your definition of deviance, which consists of only 1 term rather than 2 terms. My reply: Deviance is typically computed as a relative quantity; that is, people look at the difference in deviance. So the two definitions are equivalent.
Introduction: Jeff Ratto points me to this news article by Dean Baker reporting the work of three economists, Thomas Herndon, Michael Ash, and Robert Pollin, who found errors in a much-cited article by Carmen Reinhart and Kenneth Rogoff analyzing historical statistics of economic growth and public debt. Mike Konczal provides a clear summary; that’s where I got the above image. Errors in data processing and data analysis It turns out that Reinhart and Rogoff flubbed it. Herndon et al. write of “spreadsheet errors, omission of available data, weighting, and transcription.” The spreadsheet errors are the most embarrassing, but the other choices in data analysis seem pretty bad too. It can be tough to work with small datasets, so I have sympathy for Reinhart and Rogoff, but it does look like they were jumping to conclusions in their paper. Perhaps the urgency of the topic moved them to publish as fast as possible rather than carefully considering the impact of their data-analytic choi
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