Posts Tagged ‘ Statistical computing ’

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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Github cheat sheet

January 21, 2015
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Mike Betancourt pointed us to this page. Maybe it will be useful to you too. The post Github cheat sheet appeared first on Statistical Modeling, Causal Inference, and Social Science.

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Lewis Richardson, father of numerical weather prediction and of fractals

January 17, 2015
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Lewis Richardson, father of numerical weather prediction and of fractals

Lee Sechrest writes: If you get a chance, Wiki this guy: I [Sechrest] did and was gratifyingly reminded that I read some bits of his work in graduate school 60 years ago. Specifically, about his math models for predicting wars and his work on fractals to arrive at better estimates of the lengths of common […] The post Lewis Richardson, father of numerical weather prediction and of fractals appeared first…

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Stan comes through . . . again!

January 14, 2015
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Stan comes through . . . again!

Erikson Kaszubowski writes in: I missed your call for Stan research stories, but the recent post about stranded dolphins mentioned it again. When I read about the Crowdstorming project in your blog, I thought it would be a good project to apply my recent studies in Bayesian modeling. The project coordinators shared a big dataset […] The post Stan comes through . . . again! appeared first on Statistical Modeling,…

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Expectation propagation as a way of life

December 16, 2014
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Expectation propagation as a way of life

Aki Vehtari, Pasi Jylänki, Christian Robert, Nicolas Chopin, John Cunningham, and I write: We revisit expectation propagation (EP) as a prototype for scalable algorithms that partition big datasets into many parts and analyze each part in parallel to perform inference of shared parameters. The algorithm should be particularly efficient for hierarchical models, for which the […] The post Expectation propagation as a way of life appeared first on Statistical Modeling,…

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Next Generation Political Campaign Platform?

December 12, 2014
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Next Generation Political Campaign Platform?

[This post is by David K. Park] I’ve been imagining the next generation political campaign platform. If I were to build it, the platform would have five components: Data Collection, Sanitization, Storage, Streaming and Ingestion: This area will focus on the identification and development of the tools necessary to acquire the correct data sets for […] The post Next Generation Political Campaign Platform? appeared first on Statistical Modeling, Causal Inference,…

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Bayesian Cognitive Modeling Models Ported to Stan

December 9, 2014
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Hats off for Martin Šmíra, who has finished porting the models from Michael Lee and Eric-Jan Wagenmakers’ book Bayesian Cognitive Modeling  to Stan. Here they are: Bayesian Cognitive Modeling: Stan Example Models Martin managed to port 54 of the 57 models in the book and verified that the Stan code got the same answers as […] The post Bayesian Cognitive Modeling Models Ported to Stan appeared first on Statistical Modeling,…

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