Posts Tagged ‘ Statistical computing ’

Stan World Cup update

July 15, 2014
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Stan World Cup update

The other day I fit a simple model to estimate team abilities from World Cup outcomes. I fit the model to the signed square roots of the score differentials, using the square root on the theory that when the game is less close, it becomes more variable. 0. Background As you might recall, the estimated […] The post Stan World Cup update appeared first on Statistical Modeling, Causal Inference, and…

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Stan goes to the World Cup

July 13, 2014
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Stan goes to the World Cup

I thought it would be fun to fit a simple model in Stan to estimate the abilities of the teams in the World Cup, then I could post everything here on the blog, the whole story of the analysis from beginning to end, showing the results of spending a couple hours on a data analysis. […] The post Stan goes to the World Cup appeared first on Statistical Modeling, Causal…

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Useless Algebra, Inefficient Computation, and Opaque Model Specifications

June 28, 2014
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I (Bob, not Andrew) doubt anyone sets out to do algebra for the fun of it, implement an inefficient algorithm, or write a paper where it’s not clear what the model is. But… Why not write it in BUGS or Stan? Over on the Stan users group, Robert Grant wrote Hello everybody, I’ve just been […] The post Useless Algebra, Inefficient Computation, and Opaque Model Specifications appeared first on Statistical…

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Comment of the week

June 27, 2014
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This one, from DominikM: Really great, the simple random intercept – random slope mixed model I did yesterday now runs at least an order of magnitude faster after installing RStan 2.3 this morning. You are doing an awesome job, thanks a lot! The ...

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(Py, R, Cmd) Stan 2.3 Released

June 26, 2014
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We’re happy to announce RStan, PyStan and CmdStan 2.3. Instructions on how to install at: http://mc-stan.org/ As always, let us know if you’re having problems or have comments or suggestions. We’re hoping to roll out the next release a bit quicker this time, because we have lots of good new features that are almost ready […] The post (Py, R, Cmd) Stan 2.3 Released appeared first on Statistical Modeling, Causal…

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Judicious Bayesian Analysis to Get Frequentist Confidence Intervals

June 18, 2014
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Judicious Bayesian Analysis to Get Frequentist Confidence Intervals

Christian Bartels has a new paper, “Efficient generic integration algorithm to determine confidence intervals and p-values for hypothesis testing,” of which he writes: The paper proposes to do an analysis of observed data which may be characterized as doing a judicious Bayesian analysis of the data resulting in the determination of exact frequentist p-values and […] The post Judicious Bayesian Analysis to Get Frequentist Confidence Intervals appeared first on Statistical…

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Average predictive comparisons in R: David Chudzicki writes a package!

June 17, 2014
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Average predictive comparisons in R:  David Chudzicki writes a package!

Here it is: An R Package for Understanding Arbitrary Complex Models As complex models become widely used, it’s more important than ever to have ways of understanding them. Even when a model is built primarily for prediction (rather than primarily as an aid to understanding), we still need to know what it’s telling us. For […] The post Average predictive comparisons in R: David Chudzicki writes a package! appeared first…

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My answer: Write a little program to simulate it

June 16, 2014
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Brendon Greeff writes: I was searching for an online math blog and found your email address. I have a question relating to the draw for a sports tournament. If there are 20 teams in a tournament divided into 4 groups, and those teams are selected based on four “bands” (Band: 1-5 ranked teams, 6-10, 11-15, […] The post My answer: Write a little program to simulate it appeared first on…

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Stan is Turing Complete. So what?

June 12, 2014
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This post is by Bob Carpenter. Stan is Turing complete! There seems to a persistent misconception that Stan isn’t Turing complete.1, 2 My guess is that it stems from Stan’s (not coincidental) superficial similarity to BUGS and JAGS, which provide directed graphical model specification languages. Stan’s Turing completeness follows from its support of array data […] The post Stan is Turing Complete. So what? appeared first on Statistical Modeling, Causal…

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Superfast Metrop using data partitioning, from Marco Banterle, Clara Grazian, and Christian Robert

June 11, 2014
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Superfast not because of faster convergence but because they use a clever acceptance/rejection trick so that most of the time they don’t have to evaluate the entire target density. It’s written in terms of single-step Metropolis but I think it should be possible to do it in HMC or Nuts, in which case we could […] The post Superfast Metrop using data partitioning, from Marco Banterle, Clara Grazian, and Christian…

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