Archive of posts filed under the Bayesian Statistics category.

## More on that Fivethirtyeight prediction that Biden might only get 42% of the vote in Florida

I’ve been chewing more on the above Florida forecast from Fivethirtyeight. Their 95% interval for the election-day vote margin in Florida is something like [+16% Trump, +20% Biden], which corresponds to an approximate 95% interval of [42%, 60%] for Biden’s share of the two-party vote. This is buggin me because it’s really hard for me […]

## Florida. Comparing Economist and Fivethirtyeight forecasts.

Here’s our current forecast for Florida: We’re forecasting 52.6% of the two-party vote for Biden, with a 95% predictive interval of approx [47.0%, 58.2%], thus an approx standard error of 2.8 percentage points. The 50% interval from the normal distribution is mean +/- 2/3 s.e., thus approx [50.7%, 54.5%]. Yes, I know these predictive distributions […]

## More limitations of cross-validation and actionable recommendations

This post is by Aki. Tuomas Sivula, Måns Magnusson, and I (Aki) have a new preprint paper that analyzes one of the limitations of cross-validation Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison. Normal distribution has been used to present the uncertainty in cross-validation for a single model and in model comparison at least since […]

## Bayesian Workflow (my talk this Wed at Criteo)

Wed 26 Aug 5pm Paris time (11am NY time): The workflow of applied Bayesian statistics includes not just inference but also model building, model checking, confidence-building using fake data, troubleshooting problems with computation, model understanding, and model comparison. We move toward codifying these steps in the realistic scenario in which we are fitting many models […]

## David Spiegelhalter wants a checklist for quality control of statistical models?

David Spiegelhalter writes in with a quick question: Although I don’t do any technical stuff now, I find myself arguing for using quantified expert judgement in assessing a distribution for the size of systematic biases in estimates from lower-quality data-sources, particularly for official stats such as migration estimates, but also in other areas. We have […]

## Cmdstan 2.24.1 is released!

Rok writes: We are very happy to announce that the next release of Cmdstan (2.24.1) is now available on Github. You can find it here: https://github.com/stan-dev/cmdstan/releases/tag/v2.24.1 2 New features: A new ODE interface Functions for hidden Markov models with a discrete latent variable Elementwise pow operator and matrix power function Newton solver Support for the […]

## “I just wanted to say that for the first time in three (4!?) years of efforts, I have a way to estimate my model. . . .”

After attending a Stan workshop given by Charles Margossian at McGill University, Chris Barrington-Leigh wrote: I just wanted to say that for the first time in three (4!?) years of efforts, I have a way to estimate my model. Your workshop helped me and pushed me to be persistent enough to code up my model. […]

## 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 […]

## Comments on the new fivethirtyeight.com election forecast

A colleague pointed me to Nate Silver’s election forecast; see here and here: The headline number The Fivethirtyeight forecast gives Biden a 72% chance of winning the electoral vote, a bit less than the 89% coming from our model at the Economist. The first thing to say is that 72% and 89% can correspond to […]

## The EpiBayes research group at the University of Michigan has a postdoc opening!

Jon Zelner writes: The EpiBayes research group, led by Dr. Jon Zelner in the Dept. of Epidemiology and Center for Social Epidemiology and Population Health (CSEPH) at the University of Michigan School of Public Health seeks a postdoctoral fellow to work with us on a variety of projects relating to the transmission of SARS-CoV-2 and […]

## 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 above image, taken from a site at the University of Virginia, illustrates a problem with political punditry: There’s a demand for predictions, and there’s no shortage of outlets promising a “crystal ball” or some other sort of certainty. Along these lines, Elliott Morris points us to this very reasonable post, “Poll-Based Election Forecasts Will […]

## Regression and Other Stories translated into Python!

Ravin Kumar writes in with some great news: As readers of this blog likely know Andrew Gelman, Jennifer Hill, and Aki Vehtari have recently published a new book, Regression and Other Stories. What readers likely don’t know is that there is an active effort to translate the code examples written in R and the rstanarm […]

## StanCon 2020 is on Thursday!

For all that registered for the conference, THANK YOU! We, the organizers, are truly moved by how global and inclusive the community has become. We are currently at 230 registrants from 33 countries. And 25 scholarships were provided to people in 12 countries. Please join us. Registration is \$50. We have scholarships still available (more […]

## Somethings do not seem to spread easily – the role of simulation in statistical practice and perhaps theory.

Unlike Covid19, somethings don’t seem to spread easily and the role of simulation in statistical practice (and perhaps theory) may well be one of those. In a recent comment, Andrew provided a link to an interview about the new book Regression and Other Stories by Aki Vehtari, Andrew Gelman, and Jennifer Hill. An interview that covered […]

## Jobzzzzzz!

It’s a busy day for Bayesians. John Haman writes: The Institute for Defense Analyses – Operational Evaluation Division (OED) is looking for a Bayesian statistician to join its Test Science team.  Test Science is a group of statisticians, data scientists, and psychologists that provides expertise on experimentation to the DoD. In particular, we are looking […]

## 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. […]

## My talk this Wed 7:30pm (NY time) / Thurs 9:30am (Australian time) at the Victorian Centre for Biostatistics

The “Victorian Centre for Biostatistics,” huh? I guess maybe I should speak about Francis Galton or something. Actually, though, I’ll be giving this talk: Bayesian workflow as demonstrated with a coronavirus example We recently fit a series of models to account for uncertainty and variation in coronavirus tests (see here). We will talk about the […]

## “Frequentism-as-model”

Christian Hennig writes: Most statisticians are aware that probability models interpreted in a frequentist manner are not really true in objective reality, but only idealisations. I [Hennig] argue that this is often ignored when actually applying frequentist methods and interpreting the results, and that keeping up the awareness for the essential difference between reality and […]

## Dispelling confusion about MRP (multilevel regression and poststratification) for survey analysis

A colleague pointed me to this post from political analyst Nate Silver: At the risk of restarting the MRP [multilevel regression and poststratification] wars: For the last 3 models I’ve designed (midterms, primaries, now revisiting stuff for the general) trying to impute how a state will vote based on its demographics & polls of voters […]