Archive of posts filed under the Stan category.

## epidemia: An R package for Bayesian epidemiological modeling

Jamie Scott writes: I am a PhD candidate at Imperial College, and have been working with colleagues here to write an R package for fitting Bayesian epidemiological models using Stan. We thought this might interest readers of your blog, as it is based on work previously featured there. The package is similar in spirit to […]

## More on absolute error vs. relative error in Monte Carlo methods

This came up again in a discussion from someone asking if we can use Stan to evaluate arbitrary integrals. The integral I was given was the following: $latex \displaystyle \alpha = \int_{y \in R\textrm{-ball}} \textrm{multinormal}(y \mid 0, \sigma^2 \textrm{I}) \, \textrm{d}y$ where the $latex R$-ball is assumed to be in $latex D$ dimensions so that […]

## The typical set and its relevance to Bayesian computation

[Note: The technical discussion w.r.t. Stan is continuing on the Stan forums.] tl;dr The typical set (at some level of coverage) is the set of parameter values for which the log density (the target function) is close to its expected value. As has been much discussed, this is not the same as the posterior mode. […]

## StanCon 2020 registration is live!

Dear Stan Community,  The StanCon Organizing Committee is glad to communicate the registration for the virtual StanCon 2020 is now live. Please visit the registration page (see: https://stancon.mc-stan.org/) to purchase your tickets.  The conference will be a 24-hours event with three main sessions spanning across different time zones (British Summer Time, Eastern Time and Pacific […]

## Regression and Other Stories is available!

This will be, without a doubt, the most fun you’ll have ever had reading a statistics book. Also I think you’ll learn a few things reading it. I know that we learned a lot writing it. Regression and Other Stories started out as the first half of Data Analysis Using Regression and Multilevel/Hierarchical Models, but […]

## “Time Travel in the Brain”

Natalie Biderman and Daphna Shohamy wrote this science article for kids. Here’s the abstract: Do you believe in time travel? Every time we remember something from the past or imagine something that will happen in the future, we engage in mental time travel. Scientists discovered that, whether we mentally travel back into the past or […]

## Shortest posterior intervals

By default we use central posterior intervals. For example, the central 95% interval is the (2.5%, 97.5%) quantiles. But sometimes the central interval doesn’t seem right. This came up recently with a coronavirus testing example, where the posterior distribution for the parameter of interest was asymmetric so that the central interval is not such a […]

## Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors

Yuling, Aki, and I write: When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms can have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in the […]

## “Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe”

Seth Flaxman writes: Our work on non-pharmaceutical interventions in 11 European countries (originally Imperial report 13) is now published in Nature, Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Of note for your readers: 1) Nature has an open peer review process, so you can see the (pre-publication) peer review here. 2) Between […]

## “Worthwhile content in PNAS”

Ben Bolker sends an email with the above subject line, a link to this article, and the following content: Experimental evidence that hummingbirds can see purple … researchers used Stan to analyze the data … The article in question is called “Wild hummingbirds discriminate nonspectral colors” and is by Mary Caswell Stoddard, Harold Eyster, Benedict […]

## StanCon 2020. A 24h Global Event. (More details, new talk deadline: July 1)

Date Confirmed: Thursday, 13 August 2020 The Stan Conference will be virtual this year! We are aiming for a 24-hour conference that can bring the global Stan community together. There will be 3 scheduled blocks of time, each with a plenary talk and discussion for six contributed talks. Since the conference is virtual, we’re distributing […]

## Improving our election poll aggregation model

Luke Mansillo saw our election poll aggregation model and writes: I had a look at the Stan code and I wondered if the model that you, Merlin Heidemanns, and Elliott Morris were implementing was not really Drew Linzer’s model but really Simon Jackman’s model. I realise that Linzer published Dynamic Bayesian Forecasting of Presidential Elections […]

## Election 2020 is coming: Our poll aggregation model with Elliott Morris of the Economist

Here it is. The model is vaguely based on our past work on Bayesian combination of state polls and election forecasts but with some new twists. And, check it out: you can download our R and Stan source code and the data! Merlin Heidemanns wrote much of the code, which in turn is based on […]

## Faster than ever before: Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation

Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal write: Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as Markov chain Monte Carlo algorithms struggle with the geometry of the resulting posterior distribution and can be prohibitively slow. […]

## Sequential Bayesian Designs for Rapid Learning in COVID-19 Clinical Trials

This from Frank Harrell looks important: This trial will adopt a Bayesian framework. Continuous learning from data and computation of probabilities that are directly applicable to decision making in the face of uncertainty are hallmarks of the Bayesian approach. Bayesian sequential designs are the simplest of flexible designs, and continuous learning capitalizes on their efficiency, […]

## Super-duper online matrix derivative calculator vs. the matrix normal (for Stan)

I’m implementing the matrix normal distribution for Stan, which provides a multivariate density for a matrix with covariance factored into row and column covariances. The motivation A new colleague of mine at Flatiron’s Center for Comp Bio, Jamie Morton, is using the matrix normal to model the ocean biome. A few years ago, folks in […]

## This one’s important: Bayesian workflow for disease transmission modeling in Stan

Léo Grinsztajn, Elizaveta Semenova, Charles Margossian, and Julien Riou write: This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the COVID-19 outbreak and doing Bayesian inference. Bayesian modeling provides a principled way to quantify uncertainty and incorporate prior knowledge into the […]

## New report on coronavirus trends: “the epidemic is not under control in much of the US . . . factors modulating transmission such as rapid testing, contact tracing and behavioural precautions are crucial to offset the rise of transmission associated with loosening of social distancing . . .”

Juliette Unwin et al. write: We model the epidemics in the US at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the time-varying reproduction number (the average number of secondary infections caused by an infected person), the number of individuals that have been infected and […]