Following the informed and helpful comments from Matt Graham and Bob Carpenter on our eHMC paper [arXival] last month, we produced a revised and re-arXived version of the paper based on new experiments ran by Changye Wu and Julien Stoehr. Here are some quick replies to these comments, reproduced for convenience. (Warning: this is a […]
Category: Stan
revised empirical HMC
Following the informed and helpful comments from Matt Graham and Bob Carpenter on our eHMC paper [arXival] last month, we produced a revised and re-arXived version of the paper based on new experiments ran by Changye Wu and Julien Stoehr. Here are some quick replies to these comments, reproduced for convenience. (Warning: this is a […]
HMC step size: How does it scale with dimension?
A bunch of us were arguing about how the Hamiltonian Monte Carlo step size should scale with dimension, and so Bob did the Bob thing and just ran an experiment on the computer to figure it out. Bob writes: This is for standard normal independent in all dimensions. Note the log scale on the x […]
call for sessions and labs at Bay2sC0mp²⁰
A call to all potential participants to the incoming BayesComp 2020 conference at the University of Florida in Gainesville, Florida, 7-10 January 2020, to submit proposals [to me] for contributed sessions on everything computational or training labs [to David Rossell] on a specific language or software. The deadline is April 1 and the sessions will […]
The Stan Core Roadmap
Here’s the plan for Stan core development that Bob presented at Stancon last week (that is, back at the end of August, 2018): Part I. Rear-View Mirror Stan 2.18 Released Multi-core Processing has Landed! Multi-Process Parallelism Map Function New Built-in Functions Manuals to HTML Improved Effective Sample Size Foreach Loops Data-qualified Arguments Bug Fixes and […]
Stan This Month
So much is going on with Stan that it can be hard to keep track, so we (the Stan project) are starting a monthly update and newsletter. If you want to be included in the monthly mailing list, just type in your email here. Charles Margossian is the editor of Stan This Month and indeed […]
Computational Bayesian Statistics [book review]
This Cambridge University Press book by M. Antónia Amaral Turkman, Carlos Daniel Paulino, and Peter Müller is an enlarged translation of a set of lecture notes in Portuguese. (Warning: I have known Peter Müller from his PhD years in Purdue University and cannot pretend to perfect objectivity. For one thing, Peter once brought me frozen-solid […]
Principal Stratification on a Latent Variable (fitting a multilevel model using Stan)
Adam Sales points to this article with John Pane on principal stratification on a latent variable, and writes: Besides the fact that the paper uses Stan, and it’s about principal stratification, which you just blogged about, I thought you might like it because of its central methodological contribution. We had been trying to use computer […]
Autodiff! (for the C++ jockeys in the audience)
Here’s a cool thread from Bob Carpenter on the Stan forums, “A new continuation-based autodiff by refactoring,” on the inner workings of Stan. Enjoy.
P.S. Just for laffs, see this post from 2010 which is the earliest mention of auto…
Transforming parameters in a simple time-series model; debugging the Jacobian
So. This one is pretty simple. But the general idea could be useful to some of you. So here goes. We were fitting a model with an autocorrelation parameter, rho, which was constrained to be between 0 and 1. The model looks like this: eta_t ~ normal(rho*eta_{t-1}, sigma_res), for t = 2, 3, … T […]
NYC Meetup Thursday: Under the hood: Stan’s library, language, and algorithms
I (Bob, not Andrew!) will be doing a meetup talk next Thursday in New York City. Here’s the link with registration and location and time details (summary: pizza unboxing at 6:30 pm in SoHo): Bayesian Data Analysis Meetup: Under the hood: Stan’s library, language, and algorithms After summarizing what Stan does, this talk will focus […]
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Reproducibility and Stan
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…
Reproducibility and Stan
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…
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, […]
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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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