andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-122 knowledge-graph by maker-knowledge-mining
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Introduction: David Shor writes: My lab recently got some money to get a high-end machine. We’re mostly going to do MCMC stuff, is there anything specialized that I should keep in mind, or would any computing platform do the job? I dunno, any thoughts out there? I’ve heard that “the cloud” is becoming more popular.
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Introduction: David Shor writes: My lab recently got some money to get a high-end machine. We’re mostly going to do MCMC stuff, is there anything specialized that I should keep in mind, or would any computing platform do the job? I dunno, any thoughts out there? I’ve heard that “the cloud” is becoming more popular.
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Introduction: Good stuff.
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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.
4 0.10601525 1735 andrew gelman stats-2013-02-24-F-f-f-fake data
Introduction: Tiago Fragoso writes: Suppose I fit a two stage regression model Y = a + bx + e a = cw + d + e1 I could fit it all in one step by using MCMC for example (my model is more complicated than that, so I’ll have to do it by MCMC). However, I could fit the first regression only using MCMC because those estimates are hard to obtain and perform the second regression using least squares or a separate MCMC. So there’s an ‘one step’ inference based on doing it all at the same time and a ‘two step’ inference by fitting one and using the estimates on the further steps. What is gained or lost between both? Is anything done in this question? My response: Rather than answering your particular question, I’ll give you my generic answer, which is to simulate fake data from your model, then fit your model both ways and see how the results differ. Repeat the simulation a few thousand times and you can make all the statistical comparisons you like.
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Introduction: Just as scientists should never really have to think much about statistics, I feel that, in an ideal world, statisticians would never have to worry about computing. In the real world, though, we have to spend a lot of time building our own tools. It would be great if we could routinely run R with speed and memory limitations being less of a concern. One suggestion that sometimes arises is to run things on “the cloud.” So I was interested upon receiving this email from Niklas Frassa: Time intensive calculations, as known from life science, finance or business intelligence, can now be processed at a whole new level of speed – in the Cloud. cloudnumbers.com provides an intuitive platform that enables everyone to run time consuming calculations on clusters with more than 1000 CPUs. So far, High Performance Computing has only been accessible for large corporations and universities leading to significant competitive disadvantages for small and medium-sized companies. With cloudnu
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Introduction: David Shor writes: My lab recently got some money to get a high-end machine. We’re mostly going to do MCMC stuff, is there anything specialized that I should keep in mind, or would any computing platform do the job? I dunno, any thoughts out there? I’ve heard that “the cloud” is becoming more popular.
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Introduction: I’ve had Love the Liberry on the blogroll forever. I hadn’t checked the site for awhile and was impressed to see that they’re still at it. Great stuff—don’t ever quit! P.S. It seems that there are other librarian blogs. Pretty scary, actually! One’s enough for me.
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Introduction: I received the following email: These compressed sensing people link to Shannon’s advice . It’s refreshing when leaders of a field state that their stuff may not be a panacea. I replied: Scarily enough, I don’t know anything about this research area at all! My correspondent followed up: Meh. They proved L1 approximates L0 when design matrix is basically full rank. Now all sparsity stuff is sometimes called ‘compressed sensing’. Most of it seems to be linear interpolation, rebranded. I wrote back: But rebranding/reframing can be useful! Often reframing is a step in the direction of improvement, of better understanding one’s assumptions and goals.
4 0.67552382 2144 andrew gelman stats-2013-12-23-I hate this stuff
Introduction: Aki pointed me to this article . I’m too exhausted to argue all this in detail yet one more time, but let me just say that I hate this stuff for the reasons given in Section 5 of this paper from 1998 (based on classroom activities from 1994). I’ve hated this stuff for a long time. And I don’t think Yitzhak likes it either; see this discussion from 2005 and this from 2009.
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Introduction: Good stuff.
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Introduction: See here .
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