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Benjamin Rosenbaum @benrosenbaum.bsky.social · 14h

The existence of the bariance implies the existence of the bean

Reposted by Benjamin Rosenbaum

Privatdozent:in, Universität X @pdprekaer.bsky.social · 2d

We ask all Privatdozent:innen, Professor:innen and other colleagues, including solidaric colleagues abroad, to sign this letter and thus support our cause. You can find and sign the letter via the following link: chng.it/VhcP5tDBBY

Diese Kampagne braucht deine Unterstützung

An die Bildungsministerkonferenz: Schluss mit dem Prekariat der Privatdozent:innen!

chng.it

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Benjamin Rosenbaum @benrosenbaum.bsky.social · 2d

Alien Earth

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Benjamin Rosenbaum @benrosenbaum.bsky.social · 2d

A brief delve into the recent science history of network theory (including food webs)

Veritasium @veritasium.bsky.social · 7d

Something Strange Happens When You Trace How Connected We Are

Watch our latest video, in full, on YouTube - youtu.be/CYlon2tvywA

Something Strange Happens When You Trace How Connected We Are

YouTube video by Veritasium

youtu.be

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Benjamin Rosenbaum @benrosenbaum.bsky.social · 5d

They are calling it "The lemon of the north" on their website

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Benjamin Rosenbaum @benrosenbaum.bsky.social · 7d

Wünsche dem @verbrecherverlag.bsky.social alles Gute zum 30. Geburtstag. Aktuell auf meiner Leseliste ist der Sammelband "Klimawandel und Gesellschaftskritik"

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Benjamin Rosenbaum @benrosenbaum.bsky.social · 18d

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Reposted by Benjamin Rosenbaum

MC Stan @mc-stan.org · 21d

MC Stan is here! Follow for the latest Stan news, and tag if you want us to repost your posts about new papers, packages, courses, etc. about Stan

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Reposted by Benjamin Rosenbaum

BayesFlow @bayesflow.org · 23d

Simulations are no longer just “nice to have.” They’re reshaping how we do statistics.

Care to learn more? Check out our paper arxiv.org/abs/2503.24011, accepted for publication in the upcoming theme issue of Philosophical Transactions A.

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Reposted by Benjamin Rosenbaum

Mridul K. Thomas @mridulkthomas.bsky.social · 23d

@raviranjan.bsky.social & I are teaching a free online workshop with on experimental design for environmental scientists on the 23rd.

We'll focus on using simulations to evaluate how well different experimental designs help achieve your goals.

Please sign up & share! forms.gle/MZTxeQs4UpMr...

Poster for 90 minute workshop on HOW TO DESIGN BETTER EXPERIMENTS, by Mridul Thomas & Ravi Ranjan.

Details: September 23rd 16:00 CEST ; 14:00 UTC

Description:

Experimental designs can make or break an experiment. A good experiment has a clear goal and efficiently uses experimental resources to achieve that goal. In this workshop, we will review what experiments are for, basic and advanced principles of designing experiments, and how to use simulations to evaluate designs before actually doing the
experiment. We’ll do a moderate amount of coding in R and so experience with this would be helpful but is not required. We intend to have small-group discussions to help participants develop their own experiments, and encourage participants to think of a specific question they would like to answer with an experiment.

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Reposted by Benjamin Rosenbaum

Jan-Martin Wiarda @jmwiarda.bsky.social · 23d

Jetzt kommt es auf die Hochschulleitungen an
Nach dem Scheitern der Ampel muss die neue Koalition beim #Wissenschaftszeitvertragsgesetz endlich liefern. Gleichzeitig steigt der Druck auf die Rektorate und Präsidien.
Im Wiarda-Blog: www.jmwiarda.de/blog/2025/09...

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Benjamin Rosenbaum @benrosenbaum.bsky.social · 24d

Before becoming a computational ecologist, I was working in fluid dynamics (for airplanes). This is going to be an interesting and fun read!!

Methods in Ecology and Evolution @methodsinecoevol.bsky.social · 24d

📖Published📖

Check out our new review article 👉 The crucial role of meshing in computational fluid dynamics simulations for organic geometries in paleobiology: Describing fluid dynamics performance through best practices 🦎 🌍 🧪

buff.ly

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Reposted by Benjamin Rosenbaum

Aki Vehtari @avehtari.bsky.social · Sep 2

Stan / CmdStan 2.37 release!
blog.mc-stan.org/2025/09/02/r...
- sum_to_zero_matrix type sums to zero across both rows and columns
- simplex and *_stochastic_matrix types should be now faster and more stable
- new functions exposing the built-in constraint implementations
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Release of CmdStan 2.37

We are very happy to announce that the 2.37.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. Thi…

blog.mc-stan.org

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Benjamin Rosenbaum @benrosenbaum.bsky.social · Aug 27

Rare sighting of a theoretician in the field

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Benjamin Rosenbaum @benrosenbaum.bsky.social · Aug 26

2025 shit is hitting the fan so hard that Werner Herzog is starting social media

Login • Instagram

Welcome back to Instagram. Sign in to check out what your friends, family & interests have been capturing & sharing around the world.

www.instagram.com

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Reposted by Benjamin Rosenbaum

Jennifer S. Henke @jenniferhenke.bsky.social · Aug 25

Davon mal abgesehen, dass der Begriff "Coach" kein rechtlich geschützter ist & jede/r sich so nennen kann (ich bitte auch alle Coaches in meiner Bubble, das Folgende nicht persönlich zu nehmen) – es braucht kein x-tes Coaching zu 'mental strength in academia', 'Wege aus Wissenschaft', 1/2

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Reposted by Benjamin Rosenbaum

Julia M. Rohrer @dingdingpeng.the100.ci · Aug 25

Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities

Abstract
Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals).

Illustrated are
1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals
2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and
3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

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Reposted by Benjamin Rosenbaum

Hannah Shelley, MLIS (Metadata, Lattes & Impostor Syndrome) @hannahshelley.bsky.social · Aug 13

Since search is dead, how soon do you think Google Scholar is headed for the Google Graveyard? I'm betting it's soon, and academia is NOT prepared

Google Scholar Is Doomed

Academia built entire careers on a free Google service with zero guarantees. What could go wrong?

hannahshelley.neocities.org

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Reposted by Benjamin Rosenbaum

Aki Vehtari @avehtari.bsky.social · Aug 13

We talk more about visual posterior predictive checking and above plots in teemusailynoja.github.io/visual-predi....

The next bayesplot release will give a warning if PPC bar graphs are used with less than 6 unique outcome values, with a link to a page containing suggestions for better plots. 4/4

Recommendations for visual predictive checks in Bayesian workflow

teemusailynoja.github.io

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Benjamin Rosenbaum @benrosenbaum.bsky.social · Aug 9

Brought out the good stuff for #caturday

A tuxedo cat about to drench himself in catnip

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Benjamin Rosenbaum @benrosenbaum.bsky.social · Jul 31

It worked in the end with fighting a pen&paper index battle. But thanks for the hint, I'm quite new with nimble and didn't know this!

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Benjamin Rosenbaum @benrosenbaum.bsky.social · Jul 31

At least ChatGPT was good at telling me what does NOT work

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Benjamin Rosenbaum @benrosenbaum.bsky.social · Jul 31

Spent the better part of the morning trying to populate a non-square matrix in NIMBLE, with constants on upper triangle and variables on lower triangle and block. Extra-challenge: no if-statements and no custom running indices allowed, just slicing all the way. 🫠
#Rstats

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Reposted by Benjamin Rosenbaum

CRAN Updates @cranupdates.bsky.social · Jul 30

New on CRAN: staninside (0.0.4). View at https://CRAN.R-project.org/package=staninside

staninside: Facilitating the Use of 'Stan' Within Packages

Infrastructure and functions that can be used for integrating 'Stan' (Carpenter et al. (2017) <<a href="https://doi.org/10.18637%2Fjss.v076.i01" target="_top">doi:10.18637/jss.v076.i01</a>>) code into stand alone R packages which in turn use the 'CmdStan' engine which is often accessed through 'CmdStanR'. Details given in Stan Development Team (2025) <<a href="https://mc-stan.org/cmdstanr/" target="_top">https://mc-stan.org/cmdstanr/</a>>. Using 'CmdStanR' and pre-written 'Stan' code can make package installation easy. Using 'staninside' offers a way to cache user-compiled 'Stan' models in user-specified directories reducing the need to recompile the same model multiple times.

CRAN.R-project.org

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Benjamin Rosenbaum @benrosenbaum.bsky.social · Jul 30

R packages LaplacesDemon or BayesianTools come to my mind

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