Posts Tagged ‘ Statistical computing ’

Causal Impact from Google

March 8, 2015
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Bill Harris writes: Did you see http://blog.revolutionanalytics.com/2014/09/google-uses-r-to-calculate-roi-on-advertising-campaigns.html? Would that be something worth a joint post and discussion from you and Judea? I then wrote: Interesting. It seems to all depend on the choice of “control time series.” That said, it could still be a useful method. Bill replied: The good: Bayesian approaches made very approachable […] The post Causal Impact from Google appeared first on Statistical Modeling, Causal Inference, and…

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Interactive demonstrations for linear and Gaussian process regressions

March 7, 2015
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Interactive demonstrations for linear and Gaussian process regressions

Here’s a cool interactive demo of linear regression where you can grab the data points, move them around, and see the fitted regression line changing. There are various such apps around, but this one is particularly clean: (I’d like to credit the creator but I can’t find any attribution at the link, except that it’s […] The post Interactive demonstrations for linear and Gaussian process regressions appeared first on Statistical…

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Defaults, once set, are hard to change.

March 5, 2015
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So. Farewell then Rainbow color scheme. You reigned in Matlab Far too long. But now that You are no longer The default, Will we miss you? We can only Visualize. E. T. Thribb (17 1/2) Here’s the background.  Brad Stiritz writes: I know you’re a creator and big proponent of open-source tools. Given your strong interest […] The post Defaults, once set, are hard to change. appeared first on Statistical Modeling,…

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My talk tomorrow (Thurs) at MIT political science: Recent challenges and developments in Bayesian modeling and computation (from a political and social science perspective)

March 4, 2015
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It’s 1pm in room E53-482. I’ll talk about the usual stuff (and some of this too, I guess). The post My talk tomorrow (Thurs) at MIT political science: Recent challenges and developments in Bayesian modeling and computation (from a politica...

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One simple trick to make Stan run faster

March 3, 2015
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Did you know that Stan automatically runs in parallel (and caches compiled models) from R if you do this: source(“http://mc-stan.org/rstan/stan.R”) It’s from Stan core developer Ben Goodrich. This simple line of code has changed my li...

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Introducing shinyStan

March 2, 2015
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Introducing shinyStan

As a project for Andrew’s Statistical Communication and Graphics graduate course at Columbia, a few of us (Michael Andreae, Yuanjun Gao, Dongying Song, and I) had the goal of giving RStan’s print and plot functions a makeover. We ended up getting a bit carried away and instead we designed a graphical user interface for interactively exploring virtually […] The post Introducing shinyStan appeared first on Statistical Modeling, Causal Inference, and Social Science.

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VB-Stan: Black-box black-box variational Bayes

February 18, 2015
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Alp Kucukelbir, Rajesh Ranganath, Dave Blei, and I write: We describe an automatic variational inference method for approximating the posterior of differentiable probability models. Automatic means that the statistician only needs to define a model; the method forms a variational approximation, computes gradients using automatic differentiation and approximates expectations via Monte Carlo integration. Stochastic gradient […] The post VB-Stan: Black-box black-box variational Bayes appeared first on Statistical Modeling, Causal Inference,…

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Stan Down Under

February 15, 2015
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Stan Down Under

I (Bob, not Andrew) am in Australia until April 30. I’ll be giving some Stan-related and some data annotation talks, several of which have yet to be concretely scheduled. I’ll keep this page updated with what I’ll be up to. All of the talks other than summer school will be open to the public (the […] The post Stan Down Under appeared first on Statistical Modeling, Causal Inference, and Social…

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This has nothing to do with the Super Bowl

February 2, 2015
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Joshua Vogelstein writes: The Open Connectome Project at Johns Hopkins University invites outstanding candidates to apply for a postdoctoral or assistant research scientist position in the area of statistical machine learning for big brain imaging data. Our workflow is tightly vertically integrated, ranging from raw data to theory to answering neuroscience questions and back again. […] The post This has nothing to do with the Super Bowl appeared first on…

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Six quick tips to improve your regression modeling

January 29, 2015
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It’s Appendix A of ARM: A.1. Fit many models Think of a series of models, starting with the too-simple and continuing through to the hopelessly messy. Generally it’s a good idea to start simple. Or start complex if you’d like, but prepare to quickly drop things out and move to the simpler model to help […] The post Six quick tips to improve your regression modeling appeared first on Statistical…

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