# Posts Tagged ‘ Statistical computing ’

## Transitioning to Stan

April 14, 2014
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Kevin Cartier writes: I’ve been happily using R for a number of years now and recently came across Stan. Looks big and powerful, so I’d like to pick an appropriate project and try it out. I wondered if you could point me to a link or document that goes into the motivation for this tool […]The post Transitioning to Stan appeared first on Statistical Modeling, Causal Inference, and Social Science.

## References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling

March 29, 2014
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A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could […]The post References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling appeared…

## The maximal information coefficient

March 14, 2014
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Justin Kinney writes: I wanted to let you know that the critique Mickey Atwal and I wrote regarding equitability and the maximal information coefficient has just been published. We discussed this paper last year, under the heading, Too many MC’s not enough MIC’s, or What principles should govern attempts to summarize bivariate associations in large […]The post The maximal information coefficient appeared first on Statistical Modeling, Causal Inference, and Social…

## Stan Model of the Week: PK Calculation of IV and Oral Dosing

March 10, 2014
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[Update: Revised given comments from Wingfeet, Andrew and germo. Thanks! I'd mistakenly translated the dlnorm priors in the first version --- amazing what a difference the priors make. I also escaped the less-than and greater-than signs in the constraints in the model so they're visible. I also updated to match the thin=2 output of JAGS.] […]The post Stan Model of the Week: PK Calculation of IV and Oral Dosing appeared…

## Running into a Stan Reference by Accident

March 3, 2014
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We were talking about parallelizing MCMC and I came up with what I thought was a neat idea for parallelizing MCMC (sample with fractional prior, average samples on a per-draw basis). But then I realized this approach could get the right posterior mean or right posterior variance, but not both, depending on how the prior […]The post Running into a Stan Reference by Accident appeared first on Statistical Modeling, Causal…

## Foundations of Statistical Algorithms [book review]

February 27, 2014
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There is computational statistics and there is statistical computing. And then there is statistical algorithmic. Not the same thing, by far. This 2014 book by Weihs, Mersman and Ligges, from TU Dortmund, the later being also a member of the R Core team, stands at one end of this wide spectrum of techniques required by […]

## How to think about “identifiability” in Bayesian inference?

February 12, 2014
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We had some questions on the Stan list regarding identification. The topic arose because people were fitting models with improper posterior distributions, the kind of model where there’s a ridge in the likelihood and the parameters are not otherwise constrained. I tried to help by writing something on Bayesian identifiability for the Stan list. Then […]The post How to think about “identifiability” in Bayesian inference? appeared first on Statistical Modeling,…

## Special discount on Stan! \$999 cheaper than Revolution R!

February 4, 2014
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And we’ll throw in RStan and PyStan for free! Details here. The post Special discount on Stan! \$999 cheaper than Revolution R! appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Stupid R Tricks: Random Scope

January 29, 2014
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Andrew and I have been discussing how we’re going to define functions in Stan for defining systems of differential equations; see our evolving ode design doc; comments welcome, of course. About Scope I mentioned to Andrew I would prefer pure lexical, static scoping, as found in languages like C++ and Java. If you’re not familiar […]The post Stupid R Tricks: Random Scope appeared first on Statistical Modeling, Causal Inference, and…

## Rectangular Integration (a.k.a. The Midpoint Rule) – Conceptual Foundations and a Statistical Application in R

$Rectangular Integration (a.k.a. The Midpoint Rule) – Conceptual Foundations and a Statistical Application in R$

Introduction Continuing on the recently born series on numerical integration, this post will introduce rectangular integration.  I will describe the concept behind rectangular integration, show a function in R for how to do it, and use it to check that the distribution actually integrates to 1 over its support set.  This post follows from my […]