Posts Tagged ‘ Statistical computing ’

“Crimes Against Data”: My talk at Ohio State University this Thurs; “Solving Statistics Problems Using Stan”: My talk at the University of Michigan this Fri

September 21, 2016
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Crimes Against Data Statistics has been described as the science of uncertainty. But, paradoxically, statistical methods are often used to create a sense of certainty where none should exist. The social sciences have been rocked in recent years by highly publicized claims, published in top journals, that were reported as “statistically significant” but are implausible […] The post “Crimes Against Data”: My talk at Ohio State University this Thurs; “Solving…

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Let’s play Twister, let’s play Risk

September 14, 2016
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Let’s play Twister, let’s play Risk

Alex Terenin, Dan Simpson, and David Draper write: Some months ago we shared with you an arxiv draft of our paper, Asynchronous Distributed Gibbs Sampling.​ Through comments we’ve received, for which we’re highly grateful, we came to understand that (a) our convergence proof was wrong, and (b) we actually have two algorithms, one exact and […] The post Let’s play Twister, let’s play Risk appeared first on Statistical Modeling, Causal…

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Stan users group hits 2000 registrations

September 9, 2016
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Stan users group hits 2000 registrations

Of course, there are bound to be duplicate emails, dead emails, and people who picked up Stan, joined the list, and never came back. But still, that’s a lot of people who’ve expressed interest! It’s been an amazing ride that’s only going to get better as we learn more and continue to improve Stan’s speed […] The post Stan users group hits 2000 registrations appeared first on Statistical Modeling, Causal…

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How paracompact is that?

September 3, 2016
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How paracompact is that?

Dominic on stan-users writes: I was reading through http://arxiv.org/pdf/1410.5110v1.pdf and came across the term with which I was not familiar: “paracompact.” I wrote a short blog post about it: https://idontgetoutmuch.wordpress.com/2016/04/17/every-manifold-is-paracompact. It may be of interest to other folks reading the aforementioned paper. I would have used a partition of unity to justify the corollary myself […] The post How paracompact is that? appeared first on Statistical Modeling, Causal Inference, and…

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Fast CAR: Two weird tricks for fast conditional autoregressive models in Stan

September 3, 2016
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Fast CAR:  Two weird tricks for fast conditional autoregressive models in Stan

Max Joseph writes: Conditional autoregressive (CAR) models are popular as prior distributions for spatial random effects with areal spatial data. Historically, MCMC algorithms for CAR models have benefitted from efficient Gibbs sampling via full conditional distributions for the spatial random effects. But, these conditional specifications do not work in Stan, where the joint density needs […] The post Fast CAR: Two weird tricks for fast conditional autoregressive models in Stan…

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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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