Posts Tagged ‘ Statistical computing ’

Free workshop on Stan for pharmacometrics (Paris, 22 September 2016); preceded by (non-free) three day course on Stan for pharmacometrics

August 25, 2016
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So much for one post a day… Workshop: Stan for Pharmacometrics Day If you are interested in a free day of Stan for pharmacometrics in Paris on 22 September 2016, see the registration page: Stan for Pharmacometrics Day (free workshop) Julie Bertrand (statistical pharmacologist from Paris-Diderot and UCL) has finalized the program: When Who What […] The post Free workshop on Stan for pharmacometrics (Paris, 22 September 2016); preceded by…

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A little story of the Folk Theorem of Statistical Computing

August 12, 2016
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I know I promised I wouldn’t blog, but this one is so clean and simple. And I already wrote it for the stan-users list anyway so it’s almost no effort to post it here too: A colleague and I were working on a data analysis problem, had a very simple overdispersed Poisson regression with a […] The post A little story of the Folk Theorem of Statistical Computing appeared first…

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Some insider stuff on the Stan refactor

July 12, 2016
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From the stan-dev list, Bob wrote [and has since added brms based on comments; the * packages are ones that aren’t developed or maintained by the stan-dev team, so we only know what we hear from their authors]: The bigger picture is this, and you see the stan-dev/stan repo really spans three logical layers: stan […] The post Some insider stuff on the Stan refactor appeared first on Statistical Modeling,…

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Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab hosting)

July 7, 2016
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Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab hosting)

Jon Zelner recently developed a neat Docker packaging of Stan, R, and knitr for fully reproducible research. The first in his series of posts (with links to the next parts) is here: * Reproducibility, part 1 The post on making changes online and auto-updating results using GitLab’s continuous integration service is here: * GitLab continuous […] The post Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab…

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“Simple, Scalable and Accurate Posterior Interval Estimation”

July 1, 2016
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Cheng Li, Sanvesh Srivastava, and David Dunson write: We propose a new scalable algorithm for posterior interval estimation. Our algorithm first runs Markov chain Monte Carlo or any alternative posterior sampling algorithm in parallel for each subset posterior, with the subset posteriors proportional to the prior multiplied by the subset likelihood raised to the full […] The post “Simple, Scalable and Accurate Posterior Interval Estimation” appeared first on Statistical Modeling,…

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Short course on Bayesian data analysis and Stan 18-20 July in NYC!

June 28, 2016
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Short course on Bayesian data analysis and Stan 18-20 July in NYC!

Jonah Gabry, Vince Dorie, and I are giving a 3-day short course in two weeks. Before class everyone should install R, RStudio and RStan on their computers. (If you already have these, please update to the latest version of R and the latest version of Stan, which is 2.10.) If problems occur please join the […] The post Short course on Bayesian data analysis and Stan 18-20 July in NYC!…

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Reduced-dimensionality parameterizations for linear models with interactions

June 21, 2016
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After seeing this post by Matthew Wilson on a class of regression models called “factorization machines,” Aki writes: In a typical machine learning way, this is called “machine”, but it would be also a useful mode structure in Stan to make linear models with interactions, but with a reduced number of parameters. With a fixed […] The post Reduced-dimensionality parameterizations for linear models with interactions appeared first on Statistical Modeling,…

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Log Sum of Exponentials for Robust Sums on the Log Scale

June 11, 2016
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This is a public service announcement in the interest of more robust numerical calculations. Like matrix inverse, exponentiation is bad news. It’s prone to overflow or underflow. Just try this in R: > exp(-800) > exp(800) That’s not rounding error you see. The first one evaluates to zero (underflows) and the second to infinity (overflows). […] The post Log Sum of Exponentials for Robust Sums on the Log Scale appeared…

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Betancourt Binge (Video Lectures on HMC and Stan)

June 10, 2016
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Betancourt Binge (Video Lectures on HMC and Stan)

Even better than binging on Netflix, catch up on Michael Betancourt’s updated video lectures, just days after their live theatrical debut in Tokyo. Scalable Bayesian Inference with Hamiltonian Monte Carlo (YouTube, 1 hour) Some Bayesian Modeling Techniques in Stan (YouTube, 1 hour 40 minutes) His previous videos have received very good reviews and they’re only […] The post Betancourt Binge (Video Lectures on HMC and Stan) appeared first on Statistical…

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A Primer on Bayesian Multilevel Modeling using PyStan

June 9, 2016
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A Primer on Bayesian Multilevel Modeling using PyStan

Chris Fonnesbeck contributed our first PyStan case study (I wrote the abstract), in the form of a very nice Jupyter notebook. Daniel Lee and I had the pleasure of seeing him present it live as part of a course we were doing at Vanderbilt last week. A Primer on Bayesian Multilevel Modeling using PyStan This […] The post A Primer on Bayesian Multilevel Modeling using PyStan appeared first on Statistical…

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