andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-872 knowledge-graph by maker-knowledge-mining
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Introduction: Joseph Wilson points me to this blog on applied probability modeling. He sent me the link a couple months ago. If he’s still adding new entries, then his blog is probably already longer-lasting than most!
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same-blog 1 1.0 872 andrew gelman stats-2011-08-26-Blog on applied probability modeling
Introduction: Joseph Wilson points me to this blog on applied probability modeling. He sent me the link a couple months ago. If he’s still adding new entries, then his blog is probably already longer-lasting than most!
2 0.25887957 973 andrew gelman stats-2011-10-26-Antman again courts controversy
Introduction: Commenter Zbicyclist links to a fun article by Howard French on biologist E. O. Wilson: Wilson announced that his new book may be his last. It is not limited to the discussion of evolutionary biology, but ranges provocatively through the humanities, as well. . . . Generation after generation of students have suffered trying to “puzzle out” what great thinkers like Socrates, Plato, and Descartes had to say on the great questions of man’s nature, Wilson said, but this was of little use, because philosophy has been based on “failed models of the brain.” This reminds me of my recent remarks on the use of crude folk-psychology models as microfoundations for social sciences. The article also discusses Wilson’s recent crusade against selfish-gene-style simplifications of human and animal nature. I’m with Wilson 100% on this one. “Two brothers or eight cousins” is a cute line but it doesn’t seem to come close to describing how species or societies work, and it’s always seemed a
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Introduction: Chris Wilson points me to this visualizatio n of three physical models of the oil spill in the Gulf of Mexico. Cool (and scary) stuff. Wilson writes: One of the major advantages is that the models are 3D and show the plumes and tails beneath the surface. One of the major disadvantages is that they’re still just models.
4 0.19767377 91 andrew gelman stats-2010-06-16-RSS mess
Introduction: Apparently some of our new blog entries are appearing as old entries on the RSS feed, meaning that those of you who read the blog using RSS may be missing a lot of good stuff. We’re working on this. But, in the meantime, I recommend you click on the blog itself to see what’s been posted in the last few weeks. Enjoy.
5 0.12267449 790 andrew gelman stats-2011-07-08-Blog in motion
Introduction: In the next few days we’ll be changing the format of the blog and moving it to a new server. If you have difficulty posting comments, just wait and post them in a few days when all should be working well. (But if you can post a comment, go for it. All the old entries and comments should be reappearing in the reconstituted blog.)
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same-blog 1 0.98770869 872 andrew gelman stats-2011-08-26-Blog on applied probability modeling
Introduction: Joseph Wilson points me to this blog on applied probability modeling. He sent me the link a couple months ago. If he’s still adding new entries, then his blog is probably already longer-lasting than most!
2 0.69822472 91 andrew gelman stats-2010-06-16-RSS mess
Introduction: Apparently some of our new blog entries are appearing as old entries on the RSS feed, meaning that those of you who read the blog using RSS may be missing a lot of good stuff. We’re working on this. But, in the meantime, I recommend you click on the blog itself to see what’s been posted in the last few weeks. Enjoy.
3 0.663212 3 andrew gelman stats-2010-04-26-Bayes in the news…in a somewhat frustrating way
Introduction: I’m not sure how the New York Times defines a blog versus an article, so perhaps this post should be called “Bayes in the blogs.” Whatever. A recent NY Times article/blog post discusses a classic Bayes’ Theorem application — probability that the patient has cancer, given a “positive” mammogram — and purports to give a solution that is easy for students to understand because it doesn’t require Bayes’ Theorem, which is of course complicated and confusing. You can see my comment (#17) here.
4 0.6366905 871 andrew gelman stats-2011-08-26-Be careful what you control for . . . you just might get it!
Introduction: Robert Bell points me to this blog by Austin Frakt explaining problems in interpreting regressions that control for intermediate outcomes. As Robert notes, we discuss these issues in chapters 9 and 10. But Frakt’s example is a good one.
5 0.62242806 786 andrew gelman stats-2011-07-04-Questions about quantum computing
Introduction: I read this article by Rivka Galchen on quantum computing. Much of the article was about an eccentric scientist in his fifties named David Deutch. I’m sure the guy is brilliant but I wasn’t particularly interested in his not particularly interesting life story (apparently he’s thin and lives in Oxford). There was a brief description of quantum computing itself, which reminds me of the discussion we had a couple years ago under the heading, The laws of conditional probability are false (and the update here ). I don’t have anything new to say here; I’d just never heard of quantum computing before and it seemed relevant to our discussion. The uncertainty inherent in quantum computing seems closely related to Jouni’s idea of fully Bayesian computing , that uncertainty should be inherent in the computational structure rather than tacked on at the end. P.S. No, I’m not working on July 4th! This post is two months old, we just have a long waiting list of blog entries.
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same-blog 1 0.96003377 872 andrew gelman stats-2011-08-26-Blog on applied probability modeling
Introduction: Joseph Wilson points me to this blog on applied probability modeling. He sent me the link a couple months ago. If he’s still adding new entries, then his blog is probably already longer-lasting than most!
Introduction: Type S error: When your estimate is the wrong sign, compared to the true value of the parameter Type M error: When the magnitude of your estimate is far off, compared to the true value of the parameter More here.
3 0.91363907 779 andrew gelman stats-2011-06-25-Avoiding boundary estimates using a prior distribution as regularization
Introduction: For awhile I’ve been fitting most of my multilevel models using lmer/glmer, which gives point estimates of the group-level variance parameters (maximum marginal likelihood estimate for lmer and an approximation for glmer). I’m usually satisfied with this–sure, point estimation understates the uncertainty in model fitting, but that’s typically the least of our worries. Sometimes, though, lmer/glmer estimates group-level variances at 0 or estimates group-level correlation parameters at +/- 1. Typically, when this happens, it’s not that we’re so sure the variance is close to zero or that the correlation is close to 1 or -1; rather, the marginal likelihood does not provide a lot of information about these parameters of the group-level error distribution. I don’t want point estimates on the boundary. I don’t want to say that the unexplained variance in some dimension is exactly zero. One way to handle this problem is full Bayes: slap a prior on sigma, do your Gibbs and Metropolis
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Introduction: The answer is no, as explained in this classic article by Warren Browner and Thomas Newman from 1987. If I were to rewrite this article today, I would frame things slightly differently—referring to Type S and Type M errors rather than speaking of “the probability that the research hypothesis is true”—but overall they make good points, and I like their analogy to medical diagnostic testing.
5 0.90789962 1474 andrew gelman stats-2012-08-29-More on scaled-inverse Wishart and prior independence
Introduction: I’ve had a couple of email conversations in the past couple days on dependence in multivariate prior distributions. Modeling the degrees of freedom and scale parameters in the t distribution First, in our Stan group we’ve been discussing the choice of priors for the degrees-of-freedom parameter in the t distribution. I wrote that also there’s the question of parameterization. It does not necessarily make sense to have independent priors on the df and scale parameters. In some sense, the meaning of the scale parameter changes with the df. Prior dependence between correlation and scale parameters in the scaled inverse-Wishart model The second case of parameterization in prior distribution arose from an email I received from Chris Chatham pointing me to this exploration by Matt Simpson of the scaled inverse-Wishart prior distribution for hierarchical covariance matrices. Simpson writes: A popular prior for Σ is the inverse-Wishart distribution [ not the same as the
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