Aki prepared these slides which cover a series of topics, starting with notebooks, open code, and reproducibility of code in R and Stan; then simulation-based calibration of algorithms; then model averaging and prediction. Lots to think about here: t…

# Category: Stan

## Prior distributions for covariance matrices

Someone sent me a question regarding the inverse-Wishart prior distribution for covariance matrix, as it is the default in some software he was using. Inverse-Wishart does not make sense for prior distribution; it has problems because the shape and scale are tangled. See this paper, “Visualizing Distributions of Covariance Matrices,” by Tomoki Tokuda, Ben Goodrich, […]

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## Prior distributions for covariance matrices

Someone sent me a question regarding the inverse-Wishart prior distribution for covariance matrix, as it is the default in some software he was using. Inverse-Wishart does not make sense for prior distribution; it has problems because the shape and scale are tangled. See this paper, “Visualizing Distributions of Covariance Matrices,” by Tomoki Tokuda, Ben Goodrich, […]

The post Prior distributions for covariance matrices appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Multilevel models for multiple comparisons! Varying treatment effects!

Mark White writes: I have a question regarding using multilevel models for multiple comparisons, per your 2012 paper and many blog posts. I am in a situation where I do randomized experiments, and I have a lot of additional demographic information about people, as well. For the moment, let us just assume that all of […]

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## Fitting the Besag, York, and Mollie spatial autoregression model with discrete data

Rudy Banerjee writes: I am trying to use the Besag, York & Mollie 1991 (BYM) model to study the sociology of crime and space/time plays a vital role. Since many of the variables and parameters are discrete in nature is it possible to develop a BYM that uses an Integer Auto-regressive (INAR) process instead of […]

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## Stan development in RStudio

Check this out! RStudio now has special features for Stan: – Improved, context-aware autocompletion for Stan files and chunks – A document outline, which allows for easy navigation between Stan code blocks – Inline diagnostics, which help to find issues while you develop your Stan model – The ability to interrupt Stan parallel workers launched […]

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## Stan on the web! (thanks to RStudio)

This is big news. Thanks to RStudio, you can now run Stan effortlessly on the web. So you can get started on Stan without any investment in set-up time, no need to install C++ on your computer, etc. As Ben Goodrich writes, “RStudio Cloud is particularly useful for Stan tutorials where a lot of time […]

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## Cool postdoc position in Arizona on forestry forecasting using tree ring models!

Margaret Evans sends in this cool job ad: Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation A post-doctoral fellowship is available in the Laboratory of Tree-Ring Research (University of Arizona) to work on an NSF Macrosystems Biology-funded project assimilating together tree-ring and forest inventory data to analyze patterns and drivers of forest productivity […]

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## The hot hand—in darts!

Roland Langrock writes: Since on your blog you’ve regularly been discussing hot hand literature – which we closely followed – I’m writing to share with you a new working paper we wrote on a potential hot hand pattern in professional darts. We use state-space models in which a continuous-valued latent “hotness” variable, modeled as an […]

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## “Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models”

Aki points us to this paper by Tore Selland Kleppe, which begins: Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterisation so that the re-parameterised target distribution has close to constant […]

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## Against Winner-Take-All Attribution

This is the anti-Wolfram. I did not design or write the Stan language. I’m a user of Stan. Lots of people designed and wrote Stan, most notably Bob Carpenter (designed the language and implemented lots of the algorithms), Matt Hoffman (came up with the Nuts algorithm), and Daniel Lee (put together lots of the internals […]

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## StanCon 2018 Helsinki tutorial videos online

StanCon 2018 Helsinki tutorial videos are now online at Stan YouTube channel List of tutorials at StanCon 2018 Helsinki Basics of Bayesian inference and Stan, parts 1 + 2, Jonah Gabry & Lauren Kennedy Hierarchical models, parts 1 + 2, Ben Goodrich Stan C++ development: Adding a new function to Stan, parts 1 + 2, […]

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## StanCon Helsinki streaming live now (and tomorrow)

We’re streaming live right now!

Thursday 08:45-17:30: YouTube Link

Friday 09:00-17:00: YouTube Link

Timezone is Eastern European Summer Time (EEST) +0300 UTC

Here’s a link to the full program.

There have already been some great talks an…

## “To get started, I suggest coming up with a simple but reasonable model for missingness, then simulate fake complete data followed by a fake missingness pattern, and check that you can recover your missing-data model and your complete data model in that fake-data situation. You can then proceed from there. But if you can’t even do it with fake data, you’re sunk.”

Alex Konkel writes on a topic that never goes out of style: I’m working on a data analysis plan and am hoping you might help clarify something you wrote regarding missing data. I’m somewhat familiar with multiple imputation and some of the available methods, and I’m also becoming more familiar with Bayesian modeling like in […]

The post “To get started, I suggest coming up with a simple but reasonable model for missingness, then simulate fake complete data followed by a fake missingness pattern, and check that you can recover your missing-data model and your complete data model in that fake-data situation. You can then proceed from there. But if you can’t even do it with fake data, you’re sunk.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Three informal case studies: (1) Monte Carlo EM, (2) a new approach to C++ matrix autodiff with closures, (3) C++ serialization via parameter packs

Andrew suggested I cross-post these from the Stan forums to his blog, so here goes. Maximum marginal likelihood and posterior approximations with Monte Carlo expectation maximization: I unpack the goal of max marginal likelihood and approximate Bayes with MMAP and Laplace approximations. I then go through the basic EM algorithm (with a traditional analytic example […]

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## Three informal case studies: (1) Monte Carlo EM, (2) a new approach to C++ matrix autodiff with closures, (3) C++ serialization via parameter packs

Andrew suggested I cross-post these from the Stan forums to his blog, so here goes. Maximum marginal likelihood and posterior approximations with Monte Carlo expectation maximization: I unpack the goal of max marginal likelihood and approximate Bayes with MMAP and Laplace approximations. I then go through the basic EM algorithm (with a traditional analytic example […]

The post Three informal case studies: (1) Monte Carlo EM, (2) a new approach to C++ matrix autodiff with closures, (3) C++ serialization via parameter packs appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Thanks, NVIDIA

Andrew and I both received a note like this from NVIDIA: We have reviewed your NVIDIA GPU Grant Request and are happy support your work with the donation of (1) Titan Xp to support your research. Thanks! In case other people are interested, NVIDA’s GPU grant program provides ways for faculty or research scientists to […]

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## Thanks, NVIDIA

Andrew and I both received a note like this from NVIDIA: We have reviewed your NVIDIA GPU Grant Request and are happy support your work with the donation of (1) Titan Xp to support your research. Thanks! In case other people are interested, NVIDA’s GPU grant program provides ways for faculty or research scientists to […]

The post Thanks, NVIDIA appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Awesome MCMC animation site by Chi Feng! On Github!

Sean Talts and Bob Carpenter pointed us to this awesome MCMC animation site by Chi Feng. For instance, here’s NUTS on a banana-shaped density. This is indeed super-cool, and maybe there’s a way to connect these with Stan/ShinyStan/Bayesplot so as to automatically make movies of Stan model fits. This would be great, both to help […]

The post Awesome MCMC animation site by Chi Feng! On Github! appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Awesome MCMC animation site by Chi Feng! On Github!

Sean Talts and Bob Carpenter pointed us to this awesome MCMC animation site by Chi Feng. For instance, here’s NUTS on a banana-shaped density. This is indeed super-cool, and maybe there’s a way to connect these with Stan/ShinyStan/Bayesplot so as to automatically make movies of Stan model fits. This would be great, both to help […]

The post Awesome MCMC animation site by Chi Feng! On Github! appeared first on Statistical Modeling, Causal Inference, and Social Science.