# Posts Tagged ‘ Statistical computing ’

## “Developers Who Use Spaces Make More Money Than Those Who Use Tabs”

June 22, 2017
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Rudy Malka writes: I think you’ll enjoy this nice piece of pop regression by David Robinson: developers who use spaces make more money than those who use tabs. I’d like to know your opinion about it. At the above link, Robinson discusses a survey that allows him to compare salaries of software developers who use […] The post “Developers Who Use Spaces Make More Money Than Those Who Use Tabs”…

## SPEED: Parallelizing Stan using the Message Passing Interface (MPI)

June 16, 2017
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Sebastian Weber writes: Bayesian inference has to overcome tough computational challenges and thanks to Stan we now have a scalable MCMC sampler available. For a Stan model running NUTS, the computational cost is dominated by gradient calculations of the model log-density as a function of the parameters. While NUTS is scalable to huge parameter spaces, […] The post SPEED: Parallelizing Stan using the Message Passing Interface (MPI) appeared first on…

## Workshop on reproducibility in machine learning

June 7, 2017
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Alex Lamb writes: My colleagues and I are organizing a workshop on reproducibility and replication for the International Conference on Machine Learning (ICML). I’ve read some of your blog posts on the replication crisis in the social sciences and it seems like this workshop might be something that you’d be interested in. We have three […] The post Workshop on reproducibility in machine learning appeared first on Statistical Modeling, Causal…

## Using external C++ functions with PyStan & radial velocity exoplanets

June 3, 2017
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Dan Foreman-Mackey writes: I [Mackey] demonstrate how to use a custom C++ function in a Stan model using the Python interface PyStan. This was previously only possible using the R interface RStan (see an example here) so I hacked PyStan to make this possible in Python as well. . . . I have some existing […] The post Using external C++ functions with PyStan & radial velocity exoplanets appeared first…

## Another serious error in my published work!

June 1, 2017
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Uh oh, I’m starting to feel like that pizzagate guy . . . Here’s the background. When I talk about my serious published errors, I talk about my false theorem, I talk about my empirical analysis that was invalidated by miscoded data, I talk my election maps whose flaws were pointed out by an angry […] The post Another serious error in my published work! appeared first on Statistical Modeling,…

## Hello, world! Stan, PyMC3, and Edward

May 31, 2017
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$Hello, world! Stan, PyMC3, and Edward$

Being a computer scientist, I like to see “Hello, world!” examples of programming languages. Here, I’m going to run down how Stan, PyMC3 and Edward tackle a simple linear regression problem with a couple of predictors. No, I’m not going to take sides—I’m on a fact-finding mission. We (the Stan development team) have been trying […] The post Hello, world! Stan, PyMC3, and Edward appeared first on Statistical Modeling, Causal…

## Theoretical Statistics is the Theory of Applied Statistics: How to Think About What We Do

May 26, 2017
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Above is my talk at the 2017 New York R conference. Look, no slides! The talk went well. I think the video would be more appealing to listen to if they’d mixed in more of the crowd noise. Then you’d hear people laughing at all the right spots. P.S. Here’s my 2016 NYR talk, and […] The post Theoretical Statistics is the Theory of Applied Statistics: How to Think About…

## Visualizing your fitted Stan model using ShinyStan without interfering with your Rstudio session

May 25, 2017
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ShinyStan is great, but I don’t always use it because when you call it from R, it freezes up your R session until you close the ShinyStan window. But it turns out that it doesn’t have to be that way. Imad explains: You can open up a new session via the RStudio menu bar (Session […] The post Visualizing your fitted Stan model using ShinyStan without interfering with your Rstudio…

## Design top down, Code bottom up

May 22, 2017
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Top-down design means designing from the client application programmer interface (API) down to the code. The API lays out a precise functional specification, which says what the code will do, not how it will do it. Coding bottom up means coding the lowest-level foundations first, testing them, then continuing to build. Sometimes this requires dropping […] The post Design top down, Code bottom up appeared first on Statistical Modeling, Causal…

## A continuous hinge function for statistical modeling

May 19, 2017
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This comes up sometimes in my applied work: I want a continuous “hinge function,” something like the red curve above, connecting two straight lines in a smooth way. Why not include the sharp corner (in this case, the function y=-0.5*x if x0)? Two reasons. First, computation: Hamiltonian Monte Carlo can trip on discontinuities. Second, I […] The post A continuous hinge function for statistical modeling appeared first on Statistical Modeling,…