andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1475 knowledge-graph by maker-knowledge-mining
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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.
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1 The Stan Development Team is happy to announce the first stable versions of Stan and RStan. [sent-6, score-0.366]
2 Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. [sent-8, score-0.353]
3 It’s sort of like BUGS, but with a different language for expressing models and a different sampler for sampling from their posteriors. [sent-9, score-0.741]
4 Stan Home Page Stan’s home page is: http://mc-stan. [sent-11, score-0.259]
5 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. [sent-12, score-1.558]
6 Peruse the Manual If you’d like to learn more, the Stan User’s Guide and Reference Manual is the place to start. [sent-13, score-0.145]
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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: Kevin Cartier writes: I’ve been happily using R for a number of years now and recently came across Stan. Looks big and powerful, so I’d like to pick an appropriate project and try it out. I wondered if you could point me to a link or document that goes into the motivation for this tool (aside from the Stan user doc)? What I’d like to understand is, at what point might you look at an emergent R project and advise, “You know, that thing you’re trying to do would be a whole lot easier/simpler/more straightforward to implement with Stan.” (or words to that effect). My reply: For my collaborators in political science, Stan has been most useful for models where the data set is not huge (e.g., we might have 10,000 data points or 50,000 data points but not 10 million) but where the model is somewhat complex (for example, a model with latent time series structure). The point is that the model has enough parameters and uncertainty that you’ll want to do full Bayes (rather than some sort
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Introduction: We’re happy to announce the release of Stan C++, CmdStan, RStan, and PyStan 2.1.0. This is a minor feature release, but it is also an important bug fix release. As always, the place to start is the (all new) Stan web pages: http://mc-stan.org Major Bug in 2.0.0, 2.0.1 Stan 2.0.0 and Stan 2.0.1 introduced a bug in the implementation of the NUTS criterion that led to poor tail exploration and thus biased the posterior uncertainty downward. There was no bug in NUTS in Stan 1.3 or earlier, and 2.1 has been extensively tested and tests put in place so this problem will not recur. If you are using Stan 2.0.0 or 2.0.1, you should switch to 2.1.0 as soon as possible and rerun any models you care about. New Target Acceptance Rate Default for Stan 2.1.0 Another big change aimed at reducing posterior estimation bias was an increase in the target acceptance rate during adaptation from 0.65 to 0.80. The bad news is that iterations will take around 50% longer
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Introduction: They didn’t have room for all four graphs of the time-series decomposition so they just displayed the date-of-year graph: They rotated so the graph fit better on the page. The rotation worked for me, but I was a bit bummed that that they put the title and heading of the graph (“The birthrate tends to drop on holidays . . .”) on the left in the Mar-Apr slot, leaving no room to label Leap Day and April Fool’s. I suggested to the graphics people that they put the label at the very top and just shrink the rest of the graph by 5 or 10% so as to not take up any more total space. Then there’d be plenty of space to label Leap Day and April Fool’s. But they didn’t do it, maybe they felt that it wouldn’t look good to have the label right at the top, I dunno.
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Introduction: I let Andrew know about the comments about the defective Kindle version of BDA2 and he wrote to his editor at Chapman and Hall, Rob Calver, who wrote back with this info: I can guarantee that the Kindle version of the third edition will be a substantial improvement. We publish all of our mathematics and statistics books through Kindle now as Print Replica. This means that we send the printer pdf to Amazon and they convert into their Print Replica format, which is essentially just a pdf viewer. We have not experienced very many issues at all with this setup. Unfortunately, there was a period before Amazon launched Print Replica when they converted math/stat books into their Kindle format, and converted them very badly in some cases. Equations were held as images, making them very difficult to read. It appears this was the case with Andrew’s second edition, judging by some of the comments. The third edition [of BDA] will be available through Kindle with a short delay (for Amazo
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