Lightnews — Scholar-powered news

Osvaldo Martin @aloctavodia.bsky.social · 1d

Interested in Bayesian workflow and cross-validation?
Join Aki Vehtari’s group at Aalto University as a PhD student!
Apply through the ELLIS PhD program by Oct 31 → ellis.eu/news/ellis-p...

Recent work: users.aalto.fi/~ave/publica...

ELLIS PhD Program: Call for Applications 2025

The ELLIS mission is to create a diverse European network that promotes research excellence and advances breakthroughs in AI, as well as a pan-European PhD program to educate the next generation of AI...

ellis.eu

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Reposted by Osvaldo Martin

Aki Vehtari @avehtari.bsky.social · 3d

I'm looking for a doctoral student with Bayesian background to work on Bayesian workflow and cross-validation (see my publication list users.aalto.fi/~ave/publica... for my recent work) at Aalto University.

Apply through the ELLIS PhD program (dl October 31) ellis.eu/news/ellis-p...

ELLIS PhD Program: Call for Applications 2025

The ELLIS mission is to create a diverse European network that promotes research excellence and advances breakthroughs in AI, as well as a pan-European PhD program to educate the next generation of AI...

ellis.eu

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Reposted by Osvaldo Martin

ELLIS Institute Finland @ellisinstitute.fi · 7d

Our faculty is looking for PhD students in artificial intelligence and machine learning! Meet our new Principal Investigators and apply for a PhD position by the end of October: www.ellisinstitute.fi/PIs-2025

All the info about the ELLIS PhD program is below! 👇

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Reposted by Osvaldo Martin

Vincent Arel-Bundock @vincentab.bsky.social · 16d

The Pink Book of #MarginalEffects (aka Model to Meaning) ships next week and I've got a backlog of Zoolander memes.

Hope you're hungry for some spam in your timeline.

#RStats #PyData

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ELLIS Institute Finland @ellisinstitute.fi · 17d

Our recent media coverage 🧵👇 with features in @hs.fi, Finland's public broadcaster Yle and the @aalto.fi Keys to Growth series. cc @ellis.eu @okm.fi @csaalto.bsky.social
bsky.app/profile/elli...

ELLIS Institute Finland @ellisinstitute.fi · Aug 27

How does a small country and a new research institute take on the tech giants in AI? Top international talent, academic freedom and boundary-breaking basic research. Along with Finland's commitment 📈 to 4% of GDP for RDI, we have the makings of big impacts in AI and ML research. (More in Finnish 👇)

ELLIS Institute Finland @ellisinstitute.fi · Aug 27

"Ellis-instituutti on loistava esimerkki yliopistojen, yrityksien ja päättäjien yhteistyön hedelmästä. @samikaski.bsky.social'n mukaan Ellis vie Suomea lähemmäs globaaleja kärkitoimijoita, 'eurooppalaisen tekoälyn ytimeen'."
Lue lisää HS:ssä (€) www.hs.fi/visio/art-20... @hs.fi

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Reposted by Osvaldo Martin

Danielle Navarro @djnavarro.net · 18d

Against my better instincts, I have written some notes on how human probability judgements work and what you should expect from surveys that ask people to guess what proportion of the population is transgender. I hope never to speak of this matter again

Some notes on probability judgement – Notes from a data witch

For the love of fuck, literally nobody thinks that 20% of the population is transgender. Please stop sharing that ridiculous YouGov statistic

blog.djnavarro.net

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Reposted by Osvaldo Martin

Posit @posit.co · Sep 3

For data scientists using VS Code: a new resource just dropped to help you easily migrate your setup to Positron.

Check it out here: positron.posit.co/migrate-vsco...

#Python #VSCode #Positron

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Reposted by Osvaldo Martin

Emil Hvitfeldt @emilhvitfeldt.bsky.social · Aug 20

Happy to announce ✨quarto-revealjs-editable✨

This fully supersedes the imagemover extension, as I back then didn't realize the potential. You can now also move, resize, change font size and alignment for text in your slides

github.com/EmilHvitfeld...
#quarto #slidecrafting

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Reposted by Osvaldo Martin

Aki Vehtari @avehtari.bsky.social · Aug 13

Posterior predictive checking of binary, categorical and many ordinal models with bar graphs is useless. Even the simplest models without covariates usually have such intercept terms that category specific probabilities are learned perfectly. Can you guess which model, 1 or 2, is misspecifed? 1/4

Useless posterior predictive checking bar graphs for Models 1 and 2

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Reposted by Osvaldo Martin

juanitorduz @juanitorduz.bsky.social · Jul 9

The ArviZ core devs have done tremendous work on an improved API with a lot of novel improvements. They have put together a great migration guide: python.arviz.org/en/stable/us...

If you are an ArviZ user please take a look at it and provide feedback. Open source is all about the community 🫶

This link will take you to a page that’s not on LinkedIn

lnkd.in

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Reposted by Osvaldo Martin

Aki Vehtari @avehtari.bsky.social · Jun 26

I wrote a blog post to celebrate 10 years of loo package 🎉 (R package implementing fast Pareto smoothed importance sampling cross-validation and many other useful methods for cross-validation)

Andrew Gelman et al. @statmodeling.bsky.social · Jun 26

loo R package 10 years!
statmodeling.stat.columbia.edu/2025/06/26/l...

loo R package 10 years! | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

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Reposted by Osvaldo Martin

Pierre-Simon Laplace @learnbayesstats.bsky.social · Jun 13

Some people think R² doesn’t belong in Bayesian models
👇 David Kohns disagrees, and he has the math to back it

🎙️Ep. 134: @alex-andorra.bsky.social sits down with economist David Kohns to explore how modern Bayesian methods are reshaping time series modelling

🎧 learnbayesstats.com/episode/134-...

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Osvaldo Martin @aloctavodia.bsky.social · Jun 13

Been following Mathematics of Machine Learning since early on, great to see it out!

Most ML math books are either too applied or too abstract. This one hits the middle: rigorous, relevant, and approachable without dumbing things down. And with Python examples!

landing.packtpub.com/mathematics-...

Mathematics of Machine Learning

Data Science | Packt

landing.packtpub.com

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Reposted by Osvaldo Martin

Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · May 28

For simple estimands, treating everything as Gaussian works unreasonably well! But lots to learn from less simple estimands. @avehtari.bsky.social has a nice case study examining this (part of our forthcoming book on workflow) users.aalto.fi/~ave/casestu...

Julia M. Rohrer @dingdingpeng.the100.ci · May 28

Fitting a generalized mixed model with a gamma distribution log link and random slopes to reaction time data to arrive at precisely the same point estimate as the authors did by simply averaging and conducting a t-test:

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Reposted by Osvaldo Martin

David Kohns @davidkohns.bsky.social · Jun 1

New to the blog-game, but excited to share a piece I wrote on how to use the ARR2 prior for dynamic regression using cmdstanr: davkoh.github.io/case-studies...

It extends the idea of using R2-type priors to autoregressive state-space models (published in Bayesian Analysis) 🏴‍☠️ @avehtari.bsky.social

Dynamic Regression Case Study

davkoh.github.io

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Reposted by Osvaldo Martin

Aki Vehtari @avehtari.bsky.social · May 20

We went to Mordor and all we got were flowers and ice cream.

Bayesian workflow group was a runner-up in Aalto Open Science Award 2024. The current and past group members running-up in alphabetical order: Alejandro Catalina, Anna Riha, Asael Alonzo Matamoros, David Kohns, ...

Photo of four persons in front of a sign saying "Mordor". One of the persons is holding flowers and another one is holding an ice cream.

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Reposted by Osvaldo Martin

Serge Belongie @serge.belongie.com · Mar 30

Would you present your next NeurIPS paper in Europe instead of traveling to San Diego (US) if this was an option? Søren Hauberg (DTU) and I would love to hear the answer through this poll: (1/6)

NeurIPS participation in Europe

We seek to understand if there is interest in being able to attend NeurIPS in Europe, i.e. without travelling to San Diego, US. In the following, assume that it is possible to present accepted papers ...

docs.google.com

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Osvaldo Martin @aloctavodia.bsky.social · Mar 5

Happy to hear that you find the functions in PreliZ useful! I’m unsure I can help with the R part, but I’d love to hear if you think anything is missing in PreliZ.

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Aki Vehtari @avehtari.bsky.social · Mar 4

New paper Säilynoja, Johnson, Martin, and Vehtari, "Recommendations for visual predictive checks in Bayesian workflow" teemusailynoja.github.io/visual-predi... (also arxiv.org/abs/2503.01509)

Abstract
Introduction
A key step in the Bayesian workflow for model building is the graphical assessment of model predictions, whether these are drawn from the prior or posterior predictive distribution. The goal of these assessments is to identify whether the model is a reasonable (and ideally accurate) representation of the domain knowledge and/or observed data. There are many commonly used visual predictive checks which can be misleading if their implicit assumptions do not match the reality. Thus, there is a need for more guidance for selecting, interpreting, and diagnosing appropriate visualizations. As a visual predictive check itself can be viewed as a model fit to data, assessing when this model fails to represent the data is important for drawing well-informed conclusions.

Demonstration
We present recommendations for appropriate visual predictive checks for observations that are: continuous, discrete, or a mixture of the two. We also discuss diagnostics to aid in the selection of visual methods. Specifically, in the detection of an incorrect assumption of continuously-distributed data: identifying when data is likely to be discrete or contain discrete components, detecting and estimating possible bounds in data, and a diagnostic of the goodness-of-fit to data for density plots made through kernel density estimates.

Conclusion
We offer recommendations and diagnostic tools to mitigate ad-hoc decision-making in visual predictive checks. These contributions aim to improve the robustness and interpretability of Bayesian model criticism practices.

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Reposted by Osvaldo Martin

Pierre-Simon Laplace @learnbayesstats.bsky.social · Feb 28

⚾ @fonnesbeck.bsky.social (@pymc-labs.bsky.social, @pymc.io) will be at the Field of Play Conference giving a talk on Bayesian modelling in baseball.

😎 Our host, @alex-andorra.bsky.social , will also be attending, don’t miss this chance to connect and chat research!

🔗 www.fieldofplay.co.uk

Field of Play UK | Sports data analytics conference

Ready to level up your sports analytics game? Attend our sports data conference on 18th March run by Field of Play UK

www.fieldofplay.co.uk

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Reposted by Osvaldo Martin

Pierre-Simon Laplace @learnbayesstats.bsky.social · Feb 6

🔮 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧 𝐒𝐩𝐨𝐫𝐭𝐬 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐬 𝐂𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐆𝐚𝐦𝐞!

🎙️ In this episode, @alex-andorra.bsky.social and @fonnesbeck.bsky.social break down how Bayesian methods are revolutionizing sports analytics and why the smartest teams are embracing them

🎧 𝐋𝐢𝐬𝐭𝐞𝐧 𝐧𝐨𝐰👉 learnbayesstats.com/episode/125-...

#LBS

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Osvaldo Martin @aloctavodia.bsky.social · Feb 16

It's called ananá in Argentina and Uruguay.

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ELLIS Institute Finland @ellisinstitute.fi · Feb 4

ELLIS Institute Finland is hiring Principal Investigators in AI + machine learning. World-class resources for research incl. LUMI supercomputer, generous starting package & professorship affiliation with a university in the world’s happiest country! Apply by March 9: ellisinstitute.fi/PI-recruit

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Aki Vehtari @avehtari.bsky.social · Feb 6

If you know simulation based calibration checking (SBC), you will enjoy our new paper "Posterior SBC: Simulation-Based Calibration Checking Conditional on Data" with Teemu Säilynoja, @marvinschmitt.com and @paulbuerkner.com
arxiv.org/abs/2502.03279 1/7

Title: Posterior SBC: Simulation-Based Calibration Checking Conditional on Data

Authors: Teemu Säilynoja, Marvin Schmitt, Paul Bürkner, Aki Vehtari

Abstract: Simulation-based calibration checking (SBC) refers to the validation of an inference algorithm and model implementation through repeated inference on data simulated from a generative model. In the original and commonly used approach, the generative model uses parameters drawn from the prior, and thus the approach is testing whether the inference works for simulated data generated with parameter values plausible under that prior. This approach is natural and desirable when we want to test whether the inference works for a wide range of datasets we might observe. However, after observing data, we are interested in answering whether the inference works conditional on that particular data. In this paper, we propose posterior SBC and demonstrate how it can be used to validate the inference conditionally on observed data. We illustrate the utility of posterior SBC in three case studies: (1) A simple multilevel model; (2) a model that is governed by differential equations; and (3) a joint integrative neuroscience model which is approximated via amortized Bayesian inference with neural networks.

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Alexandre Andorra @alex-andorra.bsky.social · Jan 11

Always happy to host my dear friend @aloctavodia.bsky.social on my show! This time, we talk about #RegressionTrees, #PriorElicitation and how to teach #BayesianStats

Pierre-Simon Laplace @learnbayesstats.bsky.social · Jan 11

💯What if Bayesian modelling could be faster, more flexible, and easier to interpret?
🎧 youtu.be/7POdNknJ1Es?...

🎙️ Episode 123 is here! @alex-andorra.bsky.social chats with @aloctavodia.bsky.social about ground breaking tools and ideas that are shaping the future of Bayesian workflows.

#LBS #BART

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