Publications by andrew
Will Tiger Woods catch Jack Nicklaus? And a discussion of the virtues of using continuous data even if your goal is discrete prediction
I know next to nothing about golf. My mini-golf scores typically approach the maximum of 7 per hole, and I’ve never actually played macro-golf. I did publish a paper on golf once (A Probability Model for Golf Putting, with Deb Nolan), but it’s not so rare for people to publish papers on topics they know nothing about. Those who can’t, re...
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Decline Effect in Linguistics?
Josef Fruehwald writes: In the past few years, the empirical foundations of the social sciences, especially Psychology, have been coming under increased scrutiny and criticism. For example, there was the New Yorker piece from 2010 called “The Truth Wears Off” about the “decline effect,” or how the effect size of a phenomenon appears to de...
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Moving beyond hopeless graphics
I was at a talk awhile ago where the speaker presented tables with 4, 5, 6, even 8 significant digits even though, as is usual, only the first or second digit of each number conveyed any useful information. A graph would be better, but even if you’re too lazy to make a plot, a bit of rounding would seem to be required. I mentioned this to a co...
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Stan is fast
10,000 iterations for 4 chains on the (precompiled) efficiently-parameterized 8-schools model: > date () [1] "Thu Aug 30 22:12:53 2012" > fit3 <- stan (fit=fit2, data = schools_dat, iter = 1e4, n_chains = 4) SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1). Iteration: 10000 / 10000 [100%] (Sampling) SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2...
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Is it meaningful to talk about a probability of “65.7%” that Obama will win the election?
The other day we had a fun little discussion in the comments section of the sister blog about the appropriateness of stating forecast probabilities to the nearest tenth of a percentage point. It started when Josh Tucker posted this graph from Nate Silver: My first reaction was: this looks pretty but it’s hyper-precise. I’m a big fan of Nat...
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An epithet I can live with
Here. Indeed, I’d much rather be a legend than a myth. I just want to clarify one thing. Walter Hickey writes: [Antony Unwin and Andrew Gelman] collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics. The takeaway is that while there have been great leaps in ...
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The statistics software signal
Tyler Cowen links to a post by Sean Taylor, who writes the following about users of R: You are willing to invest in learning something difficult. You do not care about aesthetics, only availability of packages and getting results quickly. To me, R is easy and Sas is difficult. I once worked with some students who were running Sas and the output...
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R package for Bayes factors
Richard Morey writes: You and your blog readers may be interested to know that a we’ve released a major new version of the BayesFactor package to CRAN. The package computes Bayes factors for linear mixed models and regression models. Of course, I’m aware you don’t like point-null model comparisons, but the package does more than that; it al...
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Text Decryption Using MCMC
The famous probabilist and statistician Persi Diaconis wrote an article not too long ago about the “Markov chain Monte Carlo (MCMC) Revolution.” The paper describes how we are able to solve a diverse set of problems with MCMC. The first example he gives is a text decryption problem solved with a simple Metropolis Hastings sampler....
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The new Stan 1.1.1, featuring Gaussian processes!
We just released Stan 1.1.1 and RStan 1.1.1 As usual, you can find download and install instructions at: http://mc-stan.org/ This is a patch release and is fully backward compatible with Stan and RStan 1.1.0. The main thing you should notice is that the multivariate models should be much faster and all the bugs reported for 1.1.0 have been fixed...
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