andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-896 knowledge-graph by maker-knowledge-mining
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Introduction: A friend writes to me: You will be amused to know that students in our Bayesian Inference paper at 4th year found solutions to exercises from your book on-line. The amazing thing was that some of them were dumb enough to copy out solutions verbatim. However, I thought you might like to know you have done well in this class! I’m happy to hear this. I worked hard on those solutions!
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3 However, I thought you might like to know you have done well in this class! [sent-3, score-0.468]
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Introduction: A friend writes to me: You will be amused to know that students in our Bayesian Inference paper at 4th year found solutions to exercises from your book on-line. The amazing thing was that some of them were dumb enough to copy out solutions verbatim. However, I thought you might like to know you have done well in this class! I’m happy to hear this. I worked hard on those solutions!
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Introduction: Here are solutions to about 50 of the exercises from Bayesian Data Analysis. The solutions themselves haven’t been updated; I just cleaned up the file: some change in Latex had resulted in much of the computer code running off the page, so I went in and cleaned up the files. I wrote most of these in 1996, and I like them a lot. I think several of them would’ve made good journal articles, and in retrospect I wish I’d published them as such. Original material that appears first in a book (or, even worse, in homework solutions) can easily be overlooked.
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Introduction: Daniel Gerlanc asks: I’ve been reading your Regression and Multilevel Modeling book. Do you have a set of example solutions for the problems in the book? Henning Piezunka, Adam Lynton, and others have asked the same question. My universal response: I’m glad you like our book. Unfortunately, we have no solution sets. I made a bunch of solutions for my earlier book but it was so much work that I decided not to do it a second time!
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Introduction: People sometimes email asking if a solution set is available for the exercises in ARM. The answer, unfortunately, is no. Many years ago, I wrote up 50 solutions for BDA and it was a lot of work–really, it was like writing a small book in itself. The trouble is that, once I started writing them up, I wanted to do it right, to set a good example. That’s a lot more effort than simply scrawling down some quick answers.
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Introduction: A friend writes to me: You will be amused to know that students in our Bayesian Inference paper at 4th year found solutions to exercises from your book on-line. The amazing thing was that some of them were dumb enough to copy out solutions verbatim. However, I thought you might like to know you have done well in this class! I’m happy to hear this. I worked hard on those solutions!
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Introduction: In one of the final Jitts, we asked the students how the course could be improved. Some of their suggestions would work, some would not. I’m putting all the suggestions below, interpolating my responses. (Overall, I think the course went well. Please remember that the remarks below are not course evaluations; they are answers to my specific question of how the course could be better. If we’d had a Jitt asking all the ways the course was good, you’d be seeing lots of positive remarks. But that wouldn’t be particularly useful or interesting.) The best thing about the course is that the kids worked hard each week on their homeworks. OK, here are the comments and my replies: Could have been better if we did less amount but more in detail. I don’t know if this would’ve been possible. I wanted to get to the harder stuff (HMC, VB, nonparametric models) which required a certain amount of preparation. And, even so, there was not time for everything. And also, needs solut
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Introduction: A neuroeconomist asks:: Is there any literature on the Bayesian approach to simultaneous equation systems that you could suggest? (Think demand/supply in econ). My reply: I’m not up-to-date on the Bayesian econometrics literature. TTony Lancaster came out with a book a few years ago that might have some of these models. Maybe you, the commenters, have some suggestions? Measurement-error models are inherently Bayesian, seeing as they have all these latent parameters, so it seems like there should be a lot out there.
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Introduction: Eoin Lawless wrote me: I’ve been reading your blog (and John Kruschke ‘s) for several months now, as a result of starting to learn Bayesian methods from Doing Bayesian Data Analysis [I love the title of that book! --- ed.]. More recently I completed a Coursera course on Data Science. I found learning through the medium of a online course to be an amazing experience. It does not replace books, but learning new material at the same time as other people and discussing it in the forums is very motivational. Additionally it is much easier to work through exercises and projects when there is a deadline and some element of competition than to plow through the end of chapter exercises in a book. This is especially true, I believe, when the learning is for a long term goal, rather than to be used immediately in work, for example. My question: you are obviously evangelical about the benefits that Bayesian statistics brings, have you ever considered producing a Coursera (or similar) cour
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Introduction: A couple days ago we discussed some remarks by Tony O’Hagan and Jim Berger on weakly informative priors. Jim followed up on Deborah Mayo’s blog with this: Objective Bayesian priors are often improper (i.e., have infinite total mass), but this is not a problem when they are developed correctly. But not every improper prior is satisfactory. For instance, the constant prior is known to be unsatisfactory in many situations. The ‘solution’ pseudo-Bayesians often use is to choose a constant prior over a large but bounded set (a ‘weakly informative’ prior), saying it is now proper and so all is well. This is not true; if the constant prior on the whole parameter space is bad, so will be the constant prior over the bounded set. The problem is, in part, that some people confuse proper priors with subjective priors and, having learned that true subjective priors are fine, incorrectly presume that weakly informative proper priors are fine. I have a few reactions to this: 1. I agree
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Introduction: Justin Kinney writes: Since your blog has discussed the “maximal information coefficient” (MIC) of Reshef et al., I figured you might want to see the critique that Gurinder Atwal and I have posted. In short, Reshef et al.’s central claim that MIC is “equitable” is incorrect. We [Kinney and Atwal] offer mathematical proof that the definition of “equitability” Reshef et al. propose is unsatisfiable—no nontrivial dependence measure, including MIC, has this property. Replicating the simulations in their paper with modestly larger data sets validates this finding. The heuristic notion of equitability, however, can be formalized instead as a self-consistency condition closely related to the Data Processing Inequality. Mutual information satisfies this new definition of equitability but MIC does not. We therefore propose that simply estimating mutual information will, in many cases, provide the sort of dependence measure Reshef et al. seek. For background, here are my two p
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