andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1586 knowledge-graph by maker-knowledge-mining
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Introduction: Michael Landy writes: I’m in Psych and Center for Neural Science and I’m teaching a doctoral course this term in methods in psychophysics (never mind the details) at the tail end of which I’m planning on at least 2 lectures on Bayesian parameter estimation and Bayesian model comparison. So far, all the readings I have are a bit too obscure and either glancing (bits of machine-learning books: Bishop, MacKay) or too low-level. The only useful reference I’ve got is an application of these methods (a methods article of mine in a Neuroscience Methods journal). The idea is to give them a decent idea of both estimation (Jeffries priors, marginals of the posterior over the parameters) and model comparison (cross-validation, AIC, BIC, full-blown Bayesian model posterior comparisons, Bayes factor, Occam factor, blah blah blah). So: have you any suggestions for articles or chapters that might be suitable (yes, I’m aware you have an entire book that’s obviously relevant)? In the class topic
sentIndex sentText sentNum sentScore
1 Michael Landy writes: I’m in Psych and Center for Neural Science and I’m teaching a doctoral course this term in methods in psychophysics (never mind the details) at the tail end of which I’m planning on at least 2 lectures on Bayesian parameter estimation and Bayesian model comparison. [sent-1, score-1.104]
2 So far, all the readings I have are a bit too obscure and either glancing (bits of machine-learning books: Bishop, MacKay) or too low-level. [sent-2, score-0.313]
3 The only useful reference I’ve got is an application of these methods (a methods article of mine in a Neuroscience Methods journal). [sent-3, score-0.447]
4 The idea is to give them a decent idea of both estimation (Jeffries priors, marginals of the posterior over the parameters) and model comparison (cross-validation, AIC, BIC, full-blown Bayesian model posterior comparisons, Bayes factor, Occam factor, blah blah blah). [sent-4, score-1.379]
5 So: have you any suggestions for articles or chapters that might be suitable (yes, I’m aware you have an entire book that’s obviously relevant)? [sent-5, score-0.165]
6 In the class topic (psychophysics), the data being modeled are typically choice data (binomial data), but my methods paper happens to be on data from measuring movement errors (continuous data), not that any of that matters. [sent-6, score-1.083]
7 I recommend you (and your students) take a look at my 1995 article with Rubin in Sociological Methodology (if you go to my home page, go to published papers, and search, you’ll find that paper) for a thorough discussion of what we hate about this. [sent-8, score-0.293]
8 I also think Jeffreys priors are a waste of time. [sent-9, score-0.202]
9 I wouldn’t spend one moment on that in your course if I were you. [sent-10, score-0.073]
10 Regarding choice models, you could take a look at the section on choice models in chapter 6 of my book with Jennifer Hill. [sent-11, score-0.642]
11 I also have a paper in Technometrics, Multilevel modeling: What it can and cannot do . [sent-12, score-0.081]
12 Regarding the topic of model checking, you could take a look at chapter 6 of Bayesian Data Analysis. [sent-13, score-0.496]
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Introduction: Michael Landy writes: I’m in Psych and Center for Neural Science and I’m teaching a doctoral course this term in methods in psychophysics (never mind the details) at the tail end of which I’m planning on at least 2 lectures on Bayesian parameter estimation and Bayesian model comparison. So far, all the readings I have are a bit too obscure and either glancing (bits of machine-learning books: Bishop, MacKay) or too low-level. The only useful reference I’ve got is an application of these methods (a methods article of mine in a Neuroscience Methods journal). The idea is to give them a decent idea of both estimation (Jeffries priors, marginals of the posterior over the parameters) and model comparison (cross-validation, AIC, BIC, full-blown Bayesian model posterior comparisons, Bayes factor, Occam factor, blah blah blah). So: have you any suggestions for articles or chapters that might be suitable (yes, I’m aware you have an entire book that’s obviously relevant)? In the class topic
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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
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Introduction: Joan Ginther has won the Texas lottery four times. First, she won $5.4 million, then a decade later, she won $2million, then two years later $3million and in the summer of 2010, she hit a $10million jackpot. The odds of this has been calculated at one in eighteen septillion and luck like this could only come once every quadrillion years. According to Forbes, the residents of Bishop, Texas, seem to believe God was behind it all. The Texas Lottery Commission told Mr Rich that Ms Ginther must have been ‘born under a lucky star’, and that they don’t suspect foul play. Harper’s reporter Nathanial Rich recently wrote an article about Ms Ginther, which calls the the validity of her ‘luck’ into question. First, he points out, Ms Ginther is a former math professor with a PhD from Stanford University specialising in statistics. More at Daily Mail. [Edited Saturday] In comments, C Ryan King points to the original article at Harper’s and Bill Jefferys to Wired .
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Introduction: Cord Blomquist, who did a great job moving us from horrible Movable Type to nice nice WordPress, writes: I [Cord] wanted to share a little news with you related to the original work we did for you last year. When ReadyMadeWeb converted your Movable Type blog to WordPress, we got a lot of other requestes for the same service, so we started thinking about a bigger market for such a product. After a bit of research, we started work on automating the data conversion, writing rules, and exceptions to the rules, on how Movable Type and TypePad data could be translated to WordPress. After many months of work, we’re getting ready to announce TP2WP.com , a service that converts Movable Type and TypePad export files to WordPress import files, so anyone who wants to migrate to WordPress can do so easily and without losing permalinks, comments, images, or other files. By automating our service, we’ve been able to drop the price to just $99. I recommend it (and, no, Cord is not paying m
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Introduction: Anthropologist Bruce Mannheim reports that a recent well-publicized study on the genetics of native Americans, which used genetic analysis to find “at least three streams of Asian gene flow,” is in fact a confirmation of a long-known fact. Mannheim writes: This three-way distinction was known linguistically since the 1920s (for example, Sapir 1921). Basically, it’s a division among the Eskimo-Aleut languages, which straddle the Bering Straits even today, the Athabaskan languages (which were discovered to be related to a small Siberian language family only within the last few years, not by Greenberg as Wade suggested), and everything else. This is not to say that the results from genetics are unimportant, but it’s good to see how it fits with other aspects of our understanding.
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