Tootfinder

Lower Bounds on the Size of Markov Equivalence Classes
Erik Jahn, Frederick Eberhardt, Leonard J. Schulman
https://arxiv.org/abs/2506.20933 https://…

Lower Bounds on the Size of Markov Equivalence Classes
Causal discovery algorithms typically recover causal graphs only up to their Markov equivalence classes unless additional parametric assumptions are made. The sizes of these equivalence classes reflect the limits of what can be learned about the underlying causal graph from purely observational data. Under the assumptions of acyclicity, causal sufficiency, and a uniform model prior, Markov equivalence classes are known to be small on average. In this paper, we show that this is no longer the ca…

The comet-style end of flight test 9 of the #Starship as seen from Namibia: https://www.youtube.com/live/mrsEfkeczT4#t=3h43m16s (from 3:43:16) - and at the very end of this webcast there is also a still photo with much higher resolution, screenshot in https://x.com/NicosPanoptikum/status/1927534707258102063

Shut up and play yer banjo!
https://www.theatlantic.com/culture/archive/2025/05/dear-james-banjo-work-life-balance/…

Dear James: I Miss Playing the Banjo
But I also have to make a living. Is there a way to balance both?

The Markov property for $\varphi^4_3$ on the cylinder
Nikolay Barashkov, Trishen S. Gunaratnam
https://arxiv.org/abs/2506.21466 https://

The Markov property for $φ^4_3$ on the cylinder
We prove that the $φ^4_3$ model satisfies a version of Segal's axioms in the special case of three-dimensional tori and cylinders. As a consequence, we give the first proof that this model satisfies a Markov property and we characterize its boundary law up to absolutely continuous perturbations. In addition, we use Segal's axioms to give an alternative construction of the $φ^4_3$ Hamiltonian on two-dimensional tori as compared with Glimm (Comm. Math. Phys., 1968). We exploit this probabilisti…

Atomkraft weiterhin sicher.
https://ard.social/@tagesschau/114753971762173948

I've set up a backup account:
@…
I'll post from it if this one goes down.
You might like to follow it.

Homogenization of Multi-agent Learning Dynamics in Finite-state Markov Games
Yann Kerzreho (ENS Paris Saclay)
https://arxiv.org/abs/2506.21079 https://

Homogenization of Multi-agent Learning Dynamics in Finite-state Markov Games
This paper introduces a new approach for approximating the learning dynamics of multiple reinforcement learning (RL) agents interacting in a finite-state Markov game. The idea is to rescale the learning process by simultaneously reducing the learning rate and increasing the update frequency, effectively treating the agent's parameters as a slow-evolving variable influenced by the fast-mixing game state. Under mild assumptions-ergodicity of the state process and continuity of the updates-we prov…

Shut up and play yer banjo!
https://www.theatlantic.com/culture/archive/2025/05/dear-james-banjo-work-life-balance/…

Dear James: I Miss Playing the Banjo
But I also have to make a living. Is there a way to balance both?

Starmer is digging his own grave | Morning Star
https://morningstaronline.co.uk/article/starmer-digging-his-own-grave

Gaussian Invariant Markov Chain Monte Carlo
Michalis K. Titsias, Angelos Alexopoulos, Siran Liu, Petros Dellaportas
https://arxiv.org/abs/2506.21511 https:…

Gaussian Invariant Markov Chain Monte Carlo
We develop sampling methods, which consist of Gaussian invariant versions of random walk Metropolis (RWM), Metropolis adjusted Langevin algorithm (MALA) and second order Hessian or Manifold MALA. Unlike standard RWM and MALA we show that Gaussian invariant sampling can lead to ergodic estimators with improved statistical efficiency. This is due to a remarkable property of Gaussian invariance that allows us to obtain exact analytical solutions to the Poisson equation for Gaussian targets. These …