andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2020 knowledge-graph by maker-knowledge-mining
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Introduction: Over the past few months, we’ve talked about modeling with particle physicists ( Allen Caldwell ), astrophysicists ( David Hogg , who regularly comments here), and climate and energy usage modelers ( Phil Price , who regularly posts here). Big Science Black Boxes We’ve gotten pretty much the same story from all of them: their models involve “big science” components that are hugely complex and provided by outside implementations from labs like CERN or LBL. Some concrete examples for energy modeling are the TOUGH2 thermal simulator, the EnergyPlus building energy usage simulator, and global climate model (GCM) implementations. These models have the character of not only being black boxes, but taking several seconds or more to generate the equivalent of a likelihood function evaluation. So we can’t use something like Stan, because nobody has the person years required to implement something like TOUGH2 in Stan (and Stan doesn’t have the debugging or modularity tools to suppor
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1 Over the past few months, we’ve talked about modeling with particle physicists ( Allen Caldwell ), astrophysicists ( David Hogg , who regularly comments here), and climate and energy usage modelers ( Phil Price , who regularly posts here). [sent-1, score-0.736]
2 Big Science Black Boxes We’ve gotten pretty much the same story from all of them: their models involve “big science” components that are hugely complex and provided by outside implementations from labs like CERN or LBL. [sent-2, score-0.07]
3 Some concrete examples for energy modeling are the TOUGH2 thermal simulator, the EnergyPlus building energy usage simulator, and global climate model (GCM) implementations. [sent-3, score-0.653]
4 These models have the character of not only being black boxes, but taking several seconds or more to generate the equivalent of a likelihood function evaluation. [sent-4, score-0.212]
5 So we can’t use something like Stan, because nobody has the person years required to implement something like TOUGH2 in Stan (and Stan doesn’t have the debugging or modularity tools to support large-scale development anyway). [sent-5, score-0.321]
6 Even just templating out the code so that we could use automatic differentiation on it would be a huge challenge. [sent-6, score-0.14]
7 Sampling and Optimization Not surprisingly, these researchers tend to focus on methods that can be implemented using only black box log probability evaluators. [sent-7, score-0.237]
8 Phil told us that he and his colleagues at LBL use optimizers that are based on ensemble techniques like the (the Nelder-Mead method ). [sent-8, score-0.419]
9 Bayesian Analysis Toolkit (BAT) Allen Caldwell and his group (Daniel Kollar and Kevin Kröninger are listed as the core developers) are behind the Bayesian Analysis Toolkit (BAT), which is based on Metropolis. [sent-10, score-0.086]
10 BAT requires users to implement a class in C++ for the model, but it can call any kind of external libraries it wants. [sent-11, score-0.402]
11 emcee, the MCMC Hammer David Hogg and his group (Daniel Foreman-Mackey seems to be doing the heavy code lifting) are behind emcee , aka “the MCMC Hammer,” which is based on Goodman and Weare’s ensemble sampler , which was motivated by the Nelder-Mead method for optimization. [sent-12, score-0.934]
12 emcee requires users to implement a log probability function in Python, which can then call C or C++ or Fortran on the back end. [sent-13, score-0.882]
13 We plan to add the Goodman and Weare ensemble method to Stan. [sent-14, score-0.334]
14 We’re still working on how to integrate it into our sampling framework. [sent-15, score-0.143]
15 Perhaps we should’ve heeded Hadley Wickham ‘s advice, which he followed in naming “ggplot2,” which is to pick something with zero existing hits on Google. [sent-19, score-0.07]
16 ” Of course, “bugs” takes the cake in this competition. [sent-21, score-0.085]
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Introduction: Over the past few months, we’ve talked about modeling with particle physicists ( Allen Caldwell ), astrophysicists ( David Hogg , who regularly comments here), and climate and energy usage modelers ( Phil Price , who regularly posts here). Big Science Black Boxes We’ve gotten pretty much the same story from all of them: their models involve “big science” components that are hugely complex and provided by outside implementations from labs like CERN or LBL. Some concrete examples for energy modeling are the TOUGH2 thermal simulator, the EnergyPlus building energy usage simulator, and global climate model (GCM) implementations. These models have the character of not only being black boxes, but taking several seconds or more to generate the equivalent of a likelihood function evaluation. So we can’t use something like Stan, because nobody has the person years required to implement something like TOUGH2 in Stan (and Stan doesn’t have the debugging or modularity tools to suppor
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Introduction: Stan is written in C++ and can be run from the command line and from R. We’d like for Python users to be able to run Stan as well. If anyone is interested in doing this, please let us know and we’d be happy to work with you on it. Stan, like Python, is completely free and open-source. P.S. Because Stan is open-source, it of course would also be possible for people to translate Stan into Python, or to take whatever features they like from Stan and incorporate them into a Python package. That’s fine too. But we think it would make sense in addition for users to be able to run Stan directly from Python, in the same way that it can be run from R.
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Introduction: Stan 1.0.0 and RStan 1.0.0 It’s official. The Stan Development Team is happy to announce the first stable versions of Stan and RStan. What is (R)Stan? Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It’s sort of like BUGS, but with a different language for expressing models and a different sampler for sampling from their posteriors. RStan is the R interface to Stan. Stan Home Page Stan’s home page is: http://mc-stan.org/ It links everything you need to get started running Stan from the command line, from R, or from C++, including full step-by-step install instructions, a detailed user’s guide and reference manual for the modeling language, and tested ports of most of the BUGS examples. Peruse the Manual If you’d like to learn more, the Stan User’s Guide and Reference Manual is the place to start.
Introduction: Statistical Methods and Data Skepticism Data analysis today is dominated by three paradigms: null hypothesis significance testing, Bayesian inference, and exploratory data analysis. There is concern that all these methods lead to overconfidence on the part of researchers and the general public, and this concern has led to the new “data skepticism” movement. But the history of statistics is already in some sense a history of data skepticism. Concepts of bias, variance, sampling and measurement error, least-squares regression, and statistical significance can all be viewed as formalizations of data skepticism. All these methods address the concern that patterns in observed data might not generalize to the population of interest. We discuss the challenge of attaining data skepticism while avoiding data nihilism, and consider some proposed future directions. Stan Stan (mc-stan.org) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a
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Introduction: We interrupt our usual program of Ed Wegman Gregg Easterbrook Niall Ferguson mockery to deliver a serious update on our statistical computing project. Stan (“Sampling Through Adaptive Neighborhoods”) is our new C++ program (written mostly by Bob Carpenter) that draws samples from Bayesian models. Stan can take different sorts of inputs: you can write the model in a Bugs-like syntax and it goes from there, or you can write the log-posterior directly as a C++ function. Most of the computation is done using Hamiltonian Monte Carlo. HMC requires some tuning, so Matt Hoffman up and wrote a new algorithm, Nuts (the “No-U-Turn Sampler”) which optimizes HMC adaptively. In many settings, Nuts is actually more computationally efficient than the optimal static HMC! When the the Nuts paper appeared on Arxiv, Christian Robert noticed it and had some reactions . In response to Xian’s comments, Matt writes: Christian writes: I wonder about the computing time (and the “una
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Introduction: Richard McElreath writes: I’ve been translating a few ongoing data analysis projects into Stan code, mostly with success. The most important for me right now has been a hierarchical zero-inflated gamma problem. This a “hurdle” model, in which a bernoulli GLM produces zeros/nonzeros, and then a gamma GLM produces the nonzero values, using varying effects correlated with those in the bernoulli process. The data are 20 years of human foraging returns from a subsistence hunting population in Paraguay (the Ache), comprising about 15k hunts in total (Hill & Kintigh. 2009. Current Anthropology 50:369-377). Observed values are kilograms of meat returned to camp. The more complex models contain a 147-by-9 matrix of varying effects (147 unique hunters), as well as imputation of missing values. Originally, I had written the sampler myself in raw R code. It was very slow, but I knew what it was doing at least. Just before Stan version 1.0 was released, I had managed to get JAGS to do it a
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Introduction: Over the past few months, we’ve talked about modeling with particle physicists ( Allen Caldwell ), astrophysicists ( David Hogg , who regularly comments here), and climate and energy usage modelers ( Phil Price , who regularly posts here). Big Science Black Boxes We’ve gotten pretty much the same story from all of them: their models involve “big science” components that are hugely complex and provided by outside implementations from labs like CERN or LBL. Some concrete examples for energy modeling are the TOUGH2 thermal simulator, the EnergyPlus building energy usage simulator, and global climate model (GCM) implementations. These models have the character of not only being black boxes, but taking several seconds or more to generate the equivalent of a likelihood function evaluation. So we can’t use something like Stan, because nobody has the person years required to implement something like TOUGH2 in Stan (and Stan doesn’t have the debugging or modularity tools to suppor
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Introduction: Awhile ago I was cleaning out the closet and found some old unread magazines. Good stuff. As we’ve discussed before , lots of things are better read a few years late. Today I was reading the 18 Nov 2004 issue of the London Review of Books, which contained (among other things) the following: - A review by Jenny Diski of a biography of Stanley Milgram. Diski appears to want to debunk: Milgram was a whiz at devising sexy experiments, but barely interested in any theoretical basis for them. They all have the same instant attractiveness of style, and then an underlying emptiness. Huh? Michael Jordan couldn’t hit the curveball and he was reportedly an easy mark for golf hustlers but that doesn’t diminish his greatness on the basketball court. She also criticizes Milgram for being “no help at all” for solving international disputes. OK, fine. I haven’t solved any international disputes either. Milgram, though, . . . he conducted an imaginative experiment whose results stu
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Introduction: From two years ago : Awhile ago I was cleaning out the closet and found some old unread magazines. Good stuff. As we’ve discussed before , lots of things are better read a few years late. Today I was reading the 18 Nov 2004 issue of the London Review of Books, which contained (among other things) the following: - A review by Jenny Diski of a biography of Stanley Milgram. Diski appears to want to debunk: Milgram was a whiz at devising sexy experiments, but barely interested in any theoretical basis for them. They all have the same instant attractiveness of style, and then an underlying emptiness. Huh? Michael Jordan couldn’t hit the curveball and he was reportedly an easy mark for golf hustlers but that doesn’t diminish his greatness on the basketball court. She also criticizes Milgram for being “no help at all” for solving international disputes. OK, fine. I haven’t solved any international disputes either. Milgram, though, . . . he conducted an imaginative exp
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Introduction: Eytan Adar writes: I was just going through the latest draft of your paper with Anthony Unwin . I heard part of it at the talk you gave (remotely) here at UMich. I’m curious about your discussion of the Baby Name Voyager . The tool in itself is simple, attractive, and useful. No argument from me there. It’s an awesome demonstration of how subtle interactions can be very helpful (click and it zooms, type and it filters… falls perfectly into the Shneiderman visualization mantra). It satisfies a very common use case: finding appropriate names for children. That said, I can’t help but feeling that what you are really excited about is the very static analysis on last letters (you spend most of your time on this). This analysis, incidentally, is not possible to infer from the interactive application (which doesn’t support this type of filtering and pivoting). In a sense, the two visualizations don’t have anything to do with each other (other than a shared context/dataset).
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