# Posts Tagged ‘ Statistical computing ’

## 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 […]

## My recent debugging experience

January 7, 2014
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OK, so this sort of thing happens sometimes. I was working on a new idea (still working on it; if it ultimately works out—or if it doesn’t—I’ll let you know) and as part of it I was fitting little models in Stan, in a loop. I thought it would make sense to start with linear […]The post My recent debugging experience appeared first on Statistical Modeling, Causal Inference, and Social…

## 2013

January 2, 2014
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There’s lots of overlap but I put each paper into only one category.  Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later.  So you can can think of this list as representing roughly two years’ work. Political […]The post 2013 appeared first on Statistical Modeling, Causal Inference, and Social Science.

## The kluges of today are the textbook solutions of tomorrow.

December 22, 2013
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From a response on the Stan help list: Yes, indeed, I think it would be a good idea to reduce the scale on priors of the form U(0,100) or N(0,100^2). This won’t solve all problems but it can’t hurt. If the issue is that the variance parameter can be very small in the estimation, yes, […]The post The kluges of today are the textbook solutions of tomorrow. appeared first on…