Skip to content
Archive of posts filed under the Statistical computing category.

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 […]

Continuous tempering through path sampling

Yuling prepared this poster summarizing our recent work on path sampling using a continuous joint distribution. The method is really cool and represents a real advance over what Xiao-Li and I were doing in our 1998 paper. It’s still gonna have problems in high or even moderate dimensions, and ultimately I think we’re gonna need […]

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 […]

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 […]

Where do I learn about log_sum_exp, log1p, lccdf, and other numerical analysis tricks?

Richard McElreath inquires: I was helping a colleague recently fix his MATLAB code by using log_sum_exp and log1m tricks. The natural question he had was, “where do you learn this stuff?” I checked Numerical Recipes, but the statistical parts are actually pretty thin (at least in my 1994 edition). Do you know of any books/papers […]

Divisibility in statistics: Where is it needed?

The basics of Bayesian inference is p(parameters|data) proportional to p(parameters)*p(data|parameters). And, for predictions, p(predictions|data) = integral_parameters p(predictions|parameters,data)*p(parameters|data). In these expressions (and the corresponding simpler versions for maximum likelihood), “parameters” and “data” are unitary objects. Yes, it can be helpful to think of the parameter objects as being a list or vector of individual parameters; and […]

Anyone want to run this Bayesian computing conference in 2022?

OK, people think I’m obsessive with a blog with a 6-month lag, but that’s nothing compared to some statistics conferences. Mylène Bédard sends this along for anyone who might be interested: The Bayesian Computation Section of ISBA is soliciting proposals to host its flagship conference: Bayes Comp 2022 The expectation is that the meeting will […]

In my role as professional singer and ham

Pryor unhooks the deer’s skull from the wall above his still-curled-up companion. Examines it. Not a good specimen –the back half of the lower jaw’s missing, a gap that, with the open cranial cavity, makes room enough for Pryor’s head. He puts it on. – Will Eaves, Murmur So as we roll into the last […]

Yes, but did it work? Evaluating variational inference

That’s the title of a recent article by Yuling Yao, Aki Vehtari, Daniel Simpson, and myself, which presents some diagnostics for variational approximations to posterior inference: We were motivated to write this paper by the success/failure of ADVI, the automatic variational inference algorithm devised by Alp Kucukelbir et al. The success was that ADVI solved […]

Ways of knowing in computer science and statistics

Brad Groff writes: Thought you might find this post by Ferenc Huszar interesting. Commentary on how we create knowledge in machine learning research and how we resolve benchmark results with (belated) theory. Key passage: You can think of “making a a deep learning method work on a dataset” as a statistical test. I would argue […]

Answering the question, What predictors are more important?, going beyond p-value thresholding and ranking

Daniel Kapitan writes: We are in the process of writing a paper on the outcome of cataract surgery. A (very rough!) draft can be found here, to provide you with some context: Using standard classification methods (Python sklearn, with synthetic oversampling to address the class imbalance), we are able to predict a poor outcome […]

Wolfram Markdown, also called Computational Essay

I was reading Stephen Wolfram’s blog and came across this post: People are used to producing prose—and sometimes pictures—to express themselves. But in the modern age of computation, something new has become possible that I’d like to call the computational essay. I [Wolfram] have been working on building the technology to support computational essays for […]

Comments on Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection

There is a recent pre-print Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection by Quentin Gronau and Eric-Jan Wagenmakers. Wagenmakers asked for comments and so here are my comments. Short version: They report a known limitation of LOO when it’s used in a non-recommended way for model selection. They report that their experiments show that […]

Could you say that again less clearly, please? A general-purpose data garbler for applications requiring confidentiality

Ariel Rokem pointed me to this Python program by Bill Howe, Julia Stoyanovich, Haoyue Ping, Bernease Herman, and Matt Gee that will take your data matrix and produce a new data matrix that has the same size, shape, and general statistical properties but with none of the same actual numbers. The use case is when […]

Zero-excluding priors are probably a bad idea for hierarchical variance parameters

(This is Dan, but in quick mode) I was on the subway when I saw Andrew’s last post and it doesn’t strike me as a particularly great idea. So let’s take a look at the suggestion for 8 schools using a centred parameterization.  This is not as comprehensive as doing a proper simulation study, but […]

How about zero-excluding priors for hierarchical variance parameters to improve computation for full Bayesian inference?

So. For awhile now we’ve moved away from the uniform (or, worse, inverse-gamma!) prior distributions for hierarchical variance parameters. We’ve done half-Cauchy, folded t, and other options; now we’re favoring unit half-normal. We also have boundary-avoiding priors for point estimates, so that in 8-schools-type problems, the posterior mode won’t be zero. Something like the gamma(2) […]

The current state of the Stan ecosystem in R

(This post is by Jonah) Last week I posted here about the release of version 2.0.0 of the loo R package, but there have been a few other recent releases and updates worth mentioning. At the end of the post I also include some general thoughts on R package development with Stan and the growing number of […]

Postdoc opportunity at AstraZeneca in Cambridge, England, in Bayesian Machine Learning using Stan!

Here it is: Predicting drug toxicity with Bayesian machine learning models We’re currently looking for talented scientists to join our innovative academic-style Postdoc. From our centre in Cambridge, UK you’ll be in a global pharmaceutical environment, contributing to live projects right from the start. You’ll take part in a comprehensive training programme, including a focus […]

You better check yo self before you wreck yo self

We (Sean Talts, Michael Betancourt, Me, Aki, and Andrew) just uploaded a paper (code available here) that outlines a framework for verifying that an algorithm for computing a posterior distribution has been implemented correctly. It is easy to use, straightforward to implement, and ready to be implemented as part of a Bayesian workflow. This type of […]

loo 2.0 is loose

This post is by Jonah and Aki. We’re happy to announce the release of v2.0.0 of the loo R package for efficient approximate leave-one-out cross-validation (and more). For anyone unfamiliar with the package, the original motivation for its development is in our paper: Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation […]