Posts Tagged ‘ Statistical computing ’

TOP SECRET: Newly declassified documents on evaluating models based on predictive accuracy

January 30, 2016
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We recently had an email discussion among the Stan team regarding the use of predictive accuracy in evaluating computing algorithms. I thought this could be of general interest so I’m sharing it here. It started when Bob said he’d been at a meting on probabilistic programming where there was confusion on evaluation. In particular, some […] The post TOP SECRET: Newly declassified documents on evaluating models based on predictive accuracy…

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One quick tip for building trust in missing-data imputations?

January 22, 2016
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Peter Liberman writes: I’m working on a paper that, in the absence of a single survey that measured the required combination of variables, analyzes data collected by separate, uncoordinated Knowledge Networks surveys in 2003. My co-author (a social psychologist who commissioned one of the surveys) and I obtained from KN unique id numbers for all […] The post One quick tip for building trust in missing-data imputations? appeared first on…

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Jim Albert’s Baseball Blog

January 20, 2016
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Jim Albert’s Baseball Blog

Jim Albert has a baseball blog: Baseball with R I sent a link internally to people I knew were into baseball, to which Andrew replied, “I agree that it’s cool that he doesn’t just talk, he has code.” (No kidding—the latest post as of writing this was on an R package to compute value above […] The post Jim Albert’s Baseball Blog appeared first on Statistical Modeling, Causal Inference, and…

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McElreath’s Statistical Rethinking: A Bayesian Course with Examples in R and Stan

January 15, 2016
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McElreath’s Statistical Rethinking: A Bayesian Course with Examples in R and Stan

We’re not even halfway through with January, but the new year’s already rung in a new book with lots of Stan content: Richard McElreath (2016) Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman & Hall/CRC Press. This one got a thumbs up from the Stan team members who’ve read it, and […] The post McElreath’s Statistical Rethinking: A Bayesian Course with Examples in R and Stan…

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rstanarm and more!

January 14, 2016
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rstanarm and more!

Ben Goodrich writes: The rstanarm R package, which has been mentioned several times on stan-users, is now available in binary form on CRAN mirrors (unless you are using an old version of R and / or an old version of OSX). It is an R package that comes with a few precompiled Stan models — […] The post rstanarm and more! appeared first on Statistical Modeling, Causal Inference, and Social…

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Stan 2.9 is Here!

January 12, 2016
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Stan 2.9 is Here!

We’re happy to announce that Stan 2.9.0 is fully available(1) for CmdStan, RStan, and PyStan — it should also work for Stan.jl (Julia), MatlabStan, and StataStan. As usual, you can find everything you need on the Stan Home Page. The main new features are: R/MATLAB-like slicing of matrices. There’s a new chapter in the user’s […] The post Stan 2.9 is Here! appeared first on Statistical Modeling, Causal Inference, and…

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Showdown in Vegas: When the numbers differ in the third decimal place

December 30, 2015
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From the Stan users list: I have just started to look into the output of the optimizing function and it seems to give estimates slightly different than the ones that I had previously obtained through maximum likelihood estimation (using MATLAB). Can you please tell me what is the penatly that the LBFGS algorithm imposes? In […] The post Showdown in Vegas: When the numbers differ in the third decimal place…

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R sucks

December 23, 2015
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I’m doing an analysis and one of the objects I’m working on is a multidimensional array called “attitude.” I took a quick look: > dim(attitude) [1] 30 7 Huh? It’s not supposed to be 30 x 7. Whassup? I search through my scripts for a “attitude” but all I find is the three-dimensional array. Where […] The post R sucks appeared first on Statistical Modeling, Causal Inference, and Social Science.

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Working Stiff

December 16, 2015
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After a challenging development process we are happy to announce that Stan finally supports stiff ODE systems, removing one of the key obstacles in fields such as pharmacometrics and ecology.  For the experts, we’ve incorporated CVODE 2.8.2 into Stan and exposed the backward-differentiation formula solver using Newton iterations and a banded Jacobian computed exactly using our autodiff. […] The post Working Stiff appeared first on Statistical Modeling, Causal Inference, and Social Science.

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My talks at Nips

December 11, 2015
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Today (Fri 11 Dec 2005), 4:30pm, room 514a, The Statistical Crisis in Science, in Workshop on Adaptive Data Analysis Today, 4:55pm, room 513ab, on a panel in Workshop on Advances in Approximate Bayesian Inference Tomorrow (Sat), 9am, room 513ab, Advent...

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