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Archive of posts filed under the Bayesian Statistics category.

Stan short course in July

Jonah Gabry is teaching a Stan short course! He’s done it before and I’ve heard that it’s excellent. Here’s the information: Dates: Wed Jul 14 – Fri Jul 16 Location: online Learn Bayesian Data Analysis and Stan with Stan Developer Jonah Gabry The course consists of three main themes: Bayesian inference and computation; the Stan […]

Progress!

This came in a mass email: Statistical Horizons is excited to present Applied Bayesian Data Analysis taught by Dr. Roy Levy on Thursday, February 18–Saturday, February 20. In this seminar, you will get both a practical and theoretical introduction to Bayesian methods in just 3 days. Topics include: Model construction Specifying prior distributions Graphical representation […]

Webinar: Theories of Inference for Data Interactions

This post is by Eric. This Thursday, at 12 pm ET, Jessica Hullman is stopping by to talk to us about theories of inference for data interactions. You can register here. Abstract Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive and exploratory analysis, optimized for easy pattern […]

Bayesian forecasting challenge!

EJ writes: A student of mine—Maximilian Maier—has designed a brief Bayesian forecasting challenge. I think it’s a nice idea and we’re looking for people that will complete the task. The relevant information is here. The link to the study is here. Feel free to try it out. If you don’t like the survey, that’s fine […]

When MCMC fails: The advice we’re giving is wrong. Here’s what we you should be doing instead. (Hint: it’s all about the folk theorem.)

In applied Bayesian statistics we often use Markov chain Monte Carlo: a family of iterative algorithms that yield approximate draws from the posterior distribution. For example, Stan uses Hamiltonian Monte Carlo. One annoying thing about these iterative algorithms is that they can take awhile, but on the plus side this spins off all sorts of […]

He wants to test whether his distribution has infinite variance. I have other ideas . . .

Evan Warfel asks a question: Let’s say that a researcher is collecting data on people for an experiment. Furthermore, it just so happens that due to the data collection procedure, data is gathered and recorded in 100-person increments. (Making it so that the researcher effectively has a time series, and at some point t, they […]

Post-doc to work on developing Bayesian workflow tools

I (Aki) am looking for a post-doc to work on developing Bayesian workflow tools at Aalto University, Finland, and Finnish Center for Artificial Intelligence, in collaboration with Andrew, Dan Simpson, Paul Bürkner, Lauren Kennedy, Måns Magnusson, Stan developers, ArviZ developers, and others. The topic is related to many ideas discussed in Bayesian Workflow paper. You […]

Network of models

Ryan Bernstein shows this demo of a prototype of the network of models visualization in Stan. This is related to the topology of models, an idea that we’ve discussed on occasion and is a key part of statistical workflow that I don’t think is handled well by existing theory or software. What Ryan is doing […]

Short course on football (soccer) analytics using Stan

From Ioannis Ntzoufras, Dimitrios Karlis, and Leonardo Egidi. I haven’t looked at the course myself but I like the idea!

Webinar: Fast Discovery of Pairwise Interactions in High Dimensions using Bayes

This post is by Eric. This Wednesday, at 12 pm ET, Tamara Broderick is stopping by to talk to us about pairwise interactions in high dimensions. You can register here. Abstract Discovering interaction effects on a response of interest is a fundamental problem in medicine, economics, and many other disciplines. In theory, Bayesian methods for […]

Another estimate of excess deaths during the pandemic.

Elliott Morris points us to this set of estimates by Sondre Solstad of excess deaths during the pandemic. The above graph is for the whole world; they also have separate graphs by continent and by country. From the description: The Economist’s global excess-death-toll estimates are, as far as we know, the first of their kind. […]

Postdoc position in Bayesian modeling for cancer

Wesley Tansey writes: I’m recruiting a postdoc to join my lab at Memorial Sloan Kettering Cancer Center (tanseyw@mskcc.org). The role overlaps a lot with the interests of people on your blog. We’re specifically looking for people with experience in subset of the following: – Bayesian hierarchical models – Spatial statistical methods (e.g. Gaussian processes, trend […]

When are Bayesian model probabilities overconfident?

Oscar Oelrich, Shutong Ding, Måns Magnusson, Aki Vehtari, and Mattias Villani write: Bayesian model comparison is often based on the posterior distribution over the set of compared models. This distribution is often observed to concentrate on a single model even when other measures of model fit or forecasting ability indicate no strong preference. Furthermore, a […]

Estimating excess mortality in rural Bangladesh from surveys and MRP

(This post is by Yuling, not by/reviewed by Andrew) Recently I (Yuling) have contributed to a public heath project with many great collaborates: The goal is to understand the excess mortality in potential relevance to Covid-19. Before recent case surge in south Asia, we have seen stories claiming that the pandemic might have hit some low-income […]

Whatever you’re looking for, it’s somewhere in the Stan documentation and you can just google for it.

Someone writes: Do you have link to an example of Zero-inflated poisson and Zero-inflated negbin model using pure stan (not brms, nor rstanarm)? If yes, please share it with me! I had a feeling there was something in the existing documentation already! So I googled *zero inflated Stan*, and . . . yup, it’s the […]

Responding to Richard Morey on p-values and inference

Jonathan Falk points to this post by Richard Morey, who writes: I [Morey] am convinced that most experienced scientists and statisticians have internalized statistical insights that frequentist statistics attempts to formalize: how you can be fooled by randomness; how what we see can be the result of biasing mechanisms; the importance of understanding sampling distributions. […]

Probability problem involving multiple coronavirus tests in the same household

Mark Tuttle writes: Here is a potential homework problem for your students. The following is a true story. Mid-December, we have a household with five people. My wife and myself, and three who arrived from elsewhere. Subsequently, various diverse symptoms ensue – nothing too serious, but everyone is concerned, obviously. Video conference for all five […]

“Bayesian Causal Inference for Real World Interactive Systems”

David Rohde points us to this workshop: Machine learning has allowed many systems that we interact with to improve performance and personalize. An important source of information in these systems is to learn from historical actions and their success or failure in applications – which is a type of causal inference. The Bayesian approach is […]

EU proposing to regulate the use of Bayesian estimation

The European Commission just released their Proposal for a Regulation on a European approach for Artificial Intelligence. They finally get around to a definition of “AI” on page 60 of the report (link above): ‘artificial intelligence system’ (AI system) means software that is developed with one or more of the techniques and approaches listed in […]

Hierarchical modeling of excess mortality time series

Elliott writes: My boss asks me: For our model to predict excess mortality around the world, we want to calculate a confidence interval around our mean estimate for total global excess deaths. We have real excess deaths for like 60 countries, and are predicting on another 130 or so. we can easily calculate intervals for […]

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