Zobrazeno 1 - 10
of 1 997
pro vyhledávání: '"Bühlmann P."'
Autor:
Prokopenko, Andrey, Arndt, Daniel, Lebrun-Grandié, Damien, Turcksin, Bruno, Frontiere, Nicholas, Emberson, J. D., Buehlmann, Michael
ArborX is a performance portable geometric search library developed as part of the Exascale Computing Project (ECP). In this paper, we explore a collaboration between ArborX and a cosmological simulation code HACC. Large cosmological simulations on e
Externí odkaz:
http://arxiv.org/abs/2409.10743
Autor:
Kéruzoré, F., Bleem, L. E., Frontiere, N., Krishnan, N., Buehlmann, M., Emberson, J. D., Habib, S., Larsen, P.
We introduce picasso, a model designed to predict thermodynamic properties of the intracluster medium based on the properties of halos in gravity-only simulations. The predictions result from the combination of an analytical gas model, mapping gas pr
Externí odkaz:
http://arxiv.org/abs/2408.17445
Autor:
Londschien, Malte, Bühlmann, Peter
We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition. This is the first weak-instrument-robust subvector test for inst
Externí odkaz:
http://arxiv.org/abs/2407.15256
Autor:
Vitório, Isabele Souza, Buehlmann, Michael, Kovacs, Eve, Larsen, Patricia, Frontiere, Nicholas, Heitmann, Katrin
Halo core tracking is a novel concept designed to efficiently follow halo substructure in large simulations. We have recently developed this concept in gravity-only simulations to investigate the galaxy-halo connection in the context of empirical and
Externí odkaz:
http://arxiv.org/abs/2407.00268
We present the online service cosmICweb (COSMological Initial Conditions on the WEB) - the first database and web interface to store, analyze, and disseminate initial conditions for zoom simulations of objects forming in cosmological simulations: fro
Externí odkaz:
http://arxiv.org/abs/2406.02693
Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for addressing nonparametric invariance and causality learning in regressio
Externí odkaz:
http://arxiv.org/abs/2405.04715
In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited information abo
Externí odkaz:
http://arxiv.org/abs/2404.11341
We present a new method for causal discovery in linear structural vector autoregressive models. We adapt an idea designed for independent observations to the case of time series while retaining its favorable properties, i.e., explicit error control f
Externí odkaz:
http://arxiv.org/abs/2403.03778
Autor:
Pfister, Niklas, Bühlmann, Peter
We define extrapolation as any type of statistical inference on a conditional function (e.g., a conditional expectation or conditional quantile) evaluated outside of the support of the conditioning variable. This type of extrapolation occurs in many
Externí odkaz:
http://arxiv.org/abs/2402.09758
We consider the problem of statistical inference on parameters of a target population when auxiliary observations are available from related populations. We propose a flexible empirical Bayes approach that can be applied on top of any asymptotically
Externí odkaz:
http://arxiv.org/abs/2312.08485