# Posts Tagged ‘ Statistical computing ’

## Bob likes the big audience

February 17, 2018
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In response to a colleague who was a bit scared of posting some work up on the internet for all to see, Bob Carpenter writes: I like the big audience for two reasons related to computer science principles. The first benefit is the same reason it’s scary. The big audience is likely to find flaws. […] The post Bob likes the big audience appeared first on Statistical Modeling, Causal Inference,…

## Eid ma clack shaw zupoven del ba.

February 7, 2018
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$Eid ma clack shaw zupoven del ba.$

When I say “I love you”, you look accordingly skeptical – Frida Hyvönen A few years back, Bill Callahan wrote a song about the night he dreamt the perfect song. In a fever, he woke and wrote it down before going back to sleep. The next morning, as he struggled to read his handwriting, he saw […] The post Eid ma clack shaw zupoven del ba. appeared first on Statistical Modeling,…

## Andrew vs. the Multi-Armed Bandit

February 4, 2018
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Andrew and I were talking about coding up some sequential designs for A/B testing in Stan the other week. I volunteered to do the legwork and implement some examples. The literature is very accessible these days—it can be found under the subject heading “multi-armed bandits.” There’s even a Wikipedia page on multi-armed bandits that lays […] The post Andrew vs. the Multi-Armed Bandit appeared first on Statistical Modeling, Causal Inference,…

## Postdoc opening on subgroup analysis and risk-benefit analysis at Merck pharmaceuticals research lab

January 30, 2018
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Richard Baumgartner writes: We are looking for a strong postdoctoral fellow for a very interesting cutting edge project. The project requires expertise in statistical modeling and machine learning. Here is the official job ad. We are looking for candidates that are strong both analytically and computationally (excellent coding skills). In the project, we are interested […] The post Postdoc opening on subgroup analysis and risk-benefit analysis at Merck pharmaceuticals research…

## How smartly.io productized Bayesian revenue estimation with Stan

January 21, 2018
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Markus Ojala writes: Bayesian modeling is becoming mainstream in many application areas. Applying it needs still a lot of knowledge about distributions and modeling techniques but the recent development in probabilistic programming languages have made it much more tractable. Stan is a promising language that suits single analysis cases well. With the improvements in approximation […] The post How smartly.io productized Bayesian revenue estimation with Stan appeared first on Statistical…

## We were measuring the speed of Stan incorrectly—it’s faster than we thought in some cases due to antithetical sampling

January 18, 2018
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Aki points out that in cases of antithetical sampling, our effective sample size calculations were unduly truncated above at the number of iterations. It turns out the effective sample size can be greater than the number of iterations if the draws are anticorrelated. And all we really care about for speed is effective sample size […] The post We were measuring the speed of Stan incorrectly—it’s faster than we thought…

## Static sensitivity analysis: Computing robustness of Bayesian inferences to the choice of hyperparameters

January 16, 2018
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Ryan Giordano wrote: Last year at StanCon we talked about how you can differentiate under the integral to automatically calculate quantitative hyperparameter robustness for Bayesian posteriors. Since then, I’ve packaged the idea up into an R library that plays nice with Stan. You can install it from this github repo. I’m sure you’ll be pretty […] The post Static sensitivity analysis: Computing robustness of Bayesian inferences to the choice of…

## Three new domain-specific (embedded) languages with a Stan backend

January 9, 2018
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One is an accident. Two is a coincidence. Three is a pattern. Perhaps it’s no coincidence that there are three new interfaces that use Stan’s C++ implementation of adaptive Hamiltonian Monte Carlo (currently an updated version of the no-U-turn sampler). ScalaStan embeds a Stan-like language in Scala. It’s a Scala package largely (if not entirely […] The post Three new domain-specific (embedded) languages with a Stan backend appeared first on…

## “Each computer run would last 1,000-2,000 hours, and, because we didn’t really trust a program that ran so long, we ran it twice, and it verified that the results matched. I’m not sure I ever was present when a run finished.”

January 6, 2018
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Bill Harris writes: Skimming Michael Betancourt’s history of MCMC [discussed yesterday in this space] made me think: my first computer job was as a nighttime computer operator on the old Rice (R1) Computer, where I was one of several students who ran Monte Carlo programs written by (the very good) chemistry prof Dr. Zevi Salsburg […] The post “Each computer run would last 1,000-2,000 hours, and, because we didn’t really…

## How does probabilistic computation differ in physics and statistics?

January 5, 2018
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[image of Schrodinger’s cat, of course] Stan collaborator Michael Betancourt wrote an article, “The Convergence of Markov chain Monte Carlo Methods: From the Metropolis method to Hamiltonian Monte Carlo,” discussing how various ideas of computational probability moved from physics to statistics. Three things I wanted to add to Betancourt’s story: 1. My paper with Rubin […] The post How does probabilistic computation differ in physics and statistics? appeared first on…