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We’re happy to announce that all three interfaces (CmdStan, PyStan, and RStan) are up and ready to go for Stan 2.4. As usual, you can find full instructions for installation on the Stan Home Page. Here are the release notes with a list of what’s new and improved: New Features ------------ * L-BFGS optimization (now […]
The post Stan 2.4, New and Improved appeared first on Statistical Modeling, Causal Inference, and Social Science.

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X and I did some “Sampling Through Adaptive Neighborhoods” ourselves the other day and checked out the nearby grave of Stanislaw Ulam, who is buried with his wife, Françoise Ulam, and others of her family. The above image of Stanislaw and Françoise Ulam comes from this charming mini-biography from Roland Brasseur, which I found here. […]
The post Stan found using directed search appeared first on Statistical Modeling, Causal Inference, and Social Science.

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The workshop is organized by John Langford (Microsoft Research NYC), along with Alekh Agarwal and Alina Beygelzimer, and it features Liblinear, Vowpal Wabbit, Torch, Theano, and . . . you guessed it . . . Stan! Here’s the current program: 8:55am: Introduction 9:00am: Liblinear by CJ Lin. 9:30am: Vowpal Wabbit and Learning to Search by […]
The post NYC workshop 22 Aug on open source machine learning systems appeared first on Statistical Modeling, Causal Inference, and Social Science.

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Anne Pier Salverda writes: I came across this blog entry, “An Experience with a Registered Replication Project,” and thought that you would find this interesting. It’s written by Simone Schnall, a social psychologist who is the first author of an oft-cited Psych Science(!) paper (“Cleanliness reduces the severity of moral judgments”) that a group of […]
The post “An Experience with a Registered Replication Project” appeared first on Statistical
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People keep pointing me to this. P.S. I miss the old days when people would point me to bad graphs.
The post If it was good enough for Martin Luther King and Laurence Tribe . . . appeared first on Statistical Modeling, Causal Inference, and Social Science.