iiinova — where your digital self keeps evolving

Moritz Schauer @mschauer.bsky.social · 24/09/2026

#MathSky Trying to demystify Caratheodory's measure extension theorem: mschauer.github.io/share/friend...

mschauer.github.io

Moritz Schauer @mschauer.bsky.social · 21/09/2026

The σ-algebra viewpoint also gives a nice form of the Chinese remainder theorem: for pairwise coprime n₁, …, nₖ it holds σ(x mod n₁, …, x mod nₖ) = σ(x mod n₁⋯nₖ). Knowing the residues separately is exactly the same information as knowing the residue modulo the product.

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Moritz Schauer @mschauer.bsky.social · 21/09/2026

Furstenberg gave a fascinating proof that extracts the infinitude of the primes from seemingly innocuous manipulations of clopen sets. Here is a retake: The σ-algebra 𝓕ₚ = σ(n ↦ n mod p) encodes exactly the information contained in knowing n mod p. That is natural, σ-algebras model information.

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Moritz Schauer @mschauer.bsky.social · 14/08/2026

How many times does the Moon go around the Earth in a year? About 13 times relative to the stars. But Earth itself goes once around the Sun. So the roughly 12 lunar months in a year are really a 13 − 1 phenomenon — the same geometry as a circle rolling inside a larger circle.

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Moritz Schauer @mschauer.bsky.social · 13/07/2026

Modern search: “You came looking for X, but wouldn’t you rather spend some time on the more addictive Y?”

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Moritz Schauer @mschauer.bsky.social · 10/07/2026

We could try to build a very expensive high energy quantum computer ring under Paris

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Moritz Schauer @mschauer.bsky.social · 03/07/2026

Dank Kompositionalität lernt man das Einmaleins aber nicht mehr das Einmaleinmaleins

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Moritz Schauer @mschauer.bsky.social · 01/07/2026

Probability theory causes a certain mathematical inflation: random variables are functions, events are sets, and probabilities are countably additive set functions of total mass one defined on σ-algebras.

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Moritz Schauer @mschauer.bsky.social · 01/07/2026

Gaurav Arya’s presentation at PLDI'26 on "Gradient Estimation as Probabilistic Inference" bringing sound, compositional and automatic gradient estimation for expectations in probabilistic programs. Great job, Gaurav!! www.youtube.com/watch?v=0aWz...

youtube.com

[PLDI 2026] Flatirons 3 - PLDI Research Papers (Jun 19th)

YouTube video by ACM SIGPLAN

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Moritz Schauer @mschauer.bsky.social · 09/06/2026

No feeling like arguing for 40 minutes with a confident idiot, only to realize they were right for all the wrong reasons. [1.2em]

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Moritz Schauer @mschauer.bsky.social · 06/06/2026

Zorn's lemma: Transfinite induction as a service

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ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 30/04/2026

arXiv📈🤖 The Difference Between "Replicable" and "Not replicable" is not Itself Scientifically Replicable By Devezer, Buzbas

Replication studies estimate the replicability rate of scientific results by aggregating binary verdicts of experiments. Exact replications are rarely attainable, so most replication sequences are non-exact. Experiments differ in ways that matter and do not share a single data-generating process. We formalize two statistical interpretations of non-exactness. In a shared latent rate (benchmark) model, experiments are exchangeable and depend on a common random replicability rate. In a conditionally independent rates (operational) model, each experiment has its own replicability rate drawn from a population distribution. Under the benchmark model, even small variability among replicability rates induces an irreducible variance floor on the estimated mean replicability rate that no amount of replication can eliminate. Under the operational model, the degree of non-exactness is not identifiable from standard replication data, because one binary verdict per experiment carries no information about between-experiment heterogeneity. Researchers cannot tell which precision regime they are in or whether high- and low-replicability sequences can be distinguished in principle. The usual data structure cannot support reliable demarcation between "replicable" and "not replicable" results and systematically understates uncertainty, making high- and low-replicability sequences appear discriminable when they are not. We show how common sources of heterogeneity amplify these problems and demonstrate practical consequences in a reanalysis of Many Labs 4. Aggregating replicability rates across heterogeneous literatures produces averages that conflate incommensurable regimes and lack a stable interpretation. Replicability rate is not a reliable demarcation criterion. The replication crisis, if there is one, cannot be established by the methods used to declare it.

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ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 24/04/2026

arXiv📈🤖 Betting on Bets: Anytime-Valid Tests for Stochastic Dominance By Arnold, Choe, Scarsini et al

How can we monitor, in real time, whether one uncertain prospect has any upside over another? To answer this question, we develop a novel family of sequential, anytime-valid tests for stochastic dominance (SD; also known as stochastic ordering), a classical and popular notion for comparing entire distribution functions. The problem is distinct from the popular problem of testing for dominance in means, which would not capture distributional differences beyond the first moment. We first derive powerful, nonparametric e-processes that quantify evidence against the null hypothesis that one prospect is dominated by another. For first-order SD, these e-processes are constructed as a mixture of asymptotically growth-rate optimal e-variables and yield a test of power one. The approach further generalizes to sequential testing for SD beyond the first order, including any higher-order SD. Empirically, we demonstrate that the resulting sequential tests are competitive with existing non-sequential SD tests in terms of power, while achieving validity under continuous monitoring that existing methods do not. Finally, we sketch the complementary and challenging problem of testing the non-SD null hypothesis, which asks whether a prospect has a definite upside, and describe the conditions under which we can derive a nontrivial anytime-valid test.

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Will Lowe @conjugateprior.org · 11/11/2025

Yes, that's the contrast of interest The relevant observations are imo 1. MH is a causal inference problem first and a probability problem second, and 2. the association usually called collider bias is information you can make use of for decision rather than just being, as it normally is, a problem

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Moritz Schauer @mschauer.bsky.social · 11/03/2026

Dust behaves as if someone put a minus sign in front of the Laplacian: dust ends up in the corners instead of spread out.

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Darren Dahly @statsepi.bsky.social · 23/02/2026

This is basically my villain origin story. "How old are you?" (unique responses)

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depths of wikipedia @depthsofwikipedia.bsky.social · 15/01/2026

wikipedia turns 25 today! the last unenshittified major website! backbone of online info! triumph of humanity! powered by urge of unpaid randos to correct each other! somehow mostly reliable! "good thing wikipedia works in practice, because it sure doesn't work in theory" - old wiki adage

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Moritz Schauer @mschauer.bsky.social · 14/01/2026

At a technical university the steps of Pearl’s ladder are called stochastics, stochastic control and optimal transport

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Moritz Schauer @mschauer.bsky.social · 12/01/2026

Causal inference is often hidden in plain sight. In a randomized clinical trial, the setup is such that interventional and conditional distributions coincide. That is E(X | do(T = t)) = E(X | T = t).

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Moritz Schauer @mschauer.bsky.social · 09/01/2026

REMEMBERING HARRY VAN ZANTEN Botond Szabó and Aad van der Vaart in the ISBA Bulletin.

isba-bulletin.github.io

The ISBA Bulletin

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Moritz Schauer @mschauer.bsky.social · 17/06/2025

It’s giving late-game vibes of Sid Meier’s Civilization, where the player is bored and just trying to see what happens if they declare some wars before they abandon the game.

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Moritz Schauer @mschauer.bsky.social · 19/12/2025

Students learn early that correlation does not imply causation. Correct, but incomplete... Certain patterns of independence and conditional dependence constrain #causal structure very strongly. A simple example:

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Moritz Schauer @mschauer.bsky.social · 02/12/2025

Vaguely funny expression: a serious title

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Moritz Schauer @mschauer.bsky.social · 12/11/2025

God made not only the numbers but also a sequence of independent standard normal random variables.

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Moritz Schauer @mschauer.bsky.social · 10/11/2025

The (samizdat?) solution book to the classic soviet era “Problems In Mathematical Analysis” by Demidovich was called “Anti-Demidovich” by the students. 😀

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Moritz Schauer @mschauer.bsky.social · 06/11/2025

The event B having happened is not the same as the receiving the information O that the event B has happened, because you also condition on receiving the information at all in the latter case. Conflating this contributes a lot to the "paradoxa" such as boy/girl paradox, Monty Hall...

$For the applicability of Bayes' theorem, it is important when the information $B$ you condition on is actually available. Let $O \subset B$ be the event of observing $B$ with $P(O)>0$. If $O$ and $A$ are independent with respect to $P_{|B}(\cdot) = P(\cdot \mid B)$, then \[ P(A\mid O) = P(A \mid B). \]$

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Alex Tong @alextong.bsky.social · 20/10/2025

#AITHYRA, Vienna's new Biomedical AI institute, is hiring Postdocs! Come work with us. Openings in: 🔹 Generative AI 🔹 Multimodal ML 🔹 Virology 🔹 Enzyme Function Apply by Nov 20: oeaw.ac.at/aithyra/post... #PostDoc #AI #ML #Vienna #ScienceJobs

oeaw.ac.at

PostDoc Search

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Rocío Mercado Oropeza @rociomer.bsky.social · 28/10/2025

Great first day of the 3rd annual CHAIR Structured Learning Workshop @ Chalmers! 🥳 Event page & agenda: ui.ungpd.com/Events/60bfc... 1st day featuring: @betapata.bsky.social @janstuehmer.bsky.social @arnauddoucet.bsky.social @frejohk.bsky.social

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Moritz Schauer @mschauer.bsky.social · 17/09/2025

Very nice by Jun Otsuka @junotk.bsky.social and Hayato Saigo: link.springer.com/article/10.1... about causal interventions/do calculus via string diagram surgery

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Jun Otsuka @junotk.bsky.social · 31/07/2025

Our process causation paper is published in Synthese! We propose that process causation (a la Salmon, Dowe, MDC new mechanists) can be modeled using a category-theoretic framework. link.springer.com/article/10.1...

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Moritz Schauer @mschauer.bsky.social · 11/09/2025

Confounding is just the failure of the Doob conditioning functor from a Markov category into the associated category of backward-forward optics to be lax comonoidal

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Moritz Schauer @mschauer.bsky.social · 04/09/2025

Oh, hats up to Mike Hicks who authored a policy paragraph on double-blind reviewing and amphibious type systems for POPL 2013 which has stand the test of time and is used in ICSME, OOPSLA, ECOOP, SPLASH, ICFP, PLDI, POPL, CSF, CAV... pldi12.cs.purdue.edu/others/dbr-f...

pldi12.cs.purdue.edu

These guidelines were originally created by Michael Hicks for POPL 2012, and slightly modified for PLDI 2012.

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Moritz Schauer @mschauer.bsky.social · 20/08/2025

"But I am conditioning on the outcome all the time and I think I understand the world just fine!"

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Simon Olsson @smnlssn.bsky.social · 06/05/2025

2025 CHAIR Structured Learning Workshop -- Apply to attend: ui.ungpd.com/Events/60bfc...

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Moritz Schauer @mschauer.bsky.social · 08/08/2025

A golden Marburg Weidenhausen night accentuated by blue paper recycling bins…

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Moritz Schauer @mschauer.bsky.social · 14/07/2025

So this is how I learned programming when I was twelve! 😃

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Rocío Mercado Oropeza @rociomer.bsky.social · 07/07/2025

Are you passionate about AI for molecular engineering? Just two weeks left to apply to the 2 PhD positions currently open in our team at Chalmers! 🎓 For details: ailab.bio/join-us

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Eugene Vinitsky 🍒 @eugenevinitsky.bsky.social · 03/07/2025

Please, please put your hyperparameter tuning procedure into the paper. For your method and the baselines

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Moritz Schauer @mschauer.bsky.social · 26/06/2025

Yeah Moritz, *electricity*, that’s totally why you were doing this (bridges for random walks on random graphs)

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ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 12/06/2025

link 📈🤖 Parallel computations for Metropolis Markov chains with Picard maps (Grazzi, Zanella) We develop parallel algorithms for simulating zeroth-order (aka gradient-free) Metropolis Markov chains based on the Picard map. For Random Walk Metropolis Markov chains targeting log-concave distributio

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Moritz Schauer @mschauer.bsky.social · 20/06/2025

Don’t let your idea of good wine be shaped by a generation you wouldn’t trust to choose the tiles for an underpass

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Moritz Schauer @mschauer.bsky.social · 18/06/2025

Tomorrow at #BayesComp @rseyer.bsky.social with arxiv.org/abs/2504.12190 (poster presentation, 19 Jun 2025, 5.30pm - 7.30pm local time)

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Moritz Schauer @mschauer.bsky.social · 17/06/2025

It’s giving late-game vibes of Sid Meier’s Civilization, where the player is bored and just trying to see what happens if they declare some wars before they abandon the game.

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Saloni @scientificdiscovery.dev · 17/06/2025

Has nominative determinism gone too far h/t @benjaminschneider.ch

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Moritz Schauer @mschauer.bsky.social · 17/06/2025

Right, you don't need error bars on error bars. Probabilistic uncertainty about uncertainty collapses. This is the “monadic join” in probability. Instead of a coin with random bias p ∼ π, you can flip a coin with the deterministic bias μ. Just take μ = E[p]. #statistics

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Alex Thiery @alexxthiery.bsky.social · 13/06/2025

Here's how the gradient flow for minimizing KL(pi, target) looks under the Fisher-Rao metric. I thought some probability mass would be disappearing on the left and appearing on the right (i.e. teleportation), like a geodesic under the same metric, but I was very wrong... What's the right intuition?

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Paul Hünermund @p-hunermund.com · 06/06/2025

The call for papers for #CDSM2025 is out! 👇

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