B. Miller

Handbook of Bayesian Deep Learning

Claudio Agostinelli, Laurence Aitchison, Emanuel Aldea, Richard Allmendinger et autres

International audience

it, gb, fr, us, hk, de, ch, sa, dk, no, cn, nl, ca, au (code pays fourni par la source)

Handbook of Bayesian Deep Learning

Claudio Agostinelli, Laurence Aitchison, Emanuel Aldea, Richard Allmendinger et autres

it, gb, fr, us, hk, de, ch, sa, dk, no, cn, nl, ca, au (code pays fourni par la source)

Enhancing Diffusion-Based Sampling with Molecular Collective Variables

Juno Nam, Bálint Máté, Artur P. Toshev, Manasa Kaniselvan et autres

Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we …

Simulation-Based Inference: A Practical Guide

Michael Deistler, Jan Boelts, Peter Steinbach, Guy Moss et autres

A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers a principled framework for this task, but can be computationally prohibitive when models are defined …

de, fr, nl, ca (code pays fourni par la source)

sbi reloaded: a toolkit for simulation-based inference workflows

Jan Boelts, Michael Deistler, Manuel Gloeckler, Álvaro Tejero-Cantero et autres

Scientists and engineers use simulators to model empirically observed phenomena.However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge.Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed …

de, ca, us, fr, tw, gb, nl, be, it, ar (code pays fourni par la source)

sbi reloaded: a toolkit for simulation-based inference workflows

Jan Boelts, Michael Deistler, Manuel Gloeckler, Álvaro Tejero-Cantero et autres

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that …

Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Adrian Abac, R. Abbott, Haruka Abe, F. Acernese et autres

Abstract Despite the growing number of binary black hole coalescences confidently observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains …

fr (code pays fourni par la source)

Search for Gravitational-lensing Signatures in the Full Third Observing Run of the LIGO–Virgo Network

R. Abbott, H. Abe, F. Acernese, K. Ackley et autres

Abstract Gravitational lensing by massive objects along the line of sight to the source causes distortions to gravitational wave (GW) signals; such distortions may reveal information about fundamental physics, cosmology, and astrophysics. In this work, we have extended the search for lensing …

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Search for Gravitational-wave Transients Associated with Magnetar Bursts in Advanced LIGO and Advanced Virgo Data from the Third Observing Run

R. Abbott, H. Abe, F. Acernese, K. Ackley et autres

Abstract Gravitational waves are expected to be produced from neutron star oscillations associated with magnetar giant flares and short bursts. We present the results of a search for short-duration (milliseconds to seconds) and long-duration (∼100 s) transient gravitational waves from 13 magnetar …

fr (code pays fourni par la source)

GWTC-2.1: Deep extended catalog of compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run

R. Abbott, T. D. Abbott, F. Acernese, K. Ackley et autres

GWTC-2.1 is a catalog of gravitational wave events from compact binary coalescences from the first half of the third observing run of Advanced LIGO and Advanced Virgo. It improves on GWTC-2, which covered the same period but with less refined analysis methods. …

us, it, au, de, in, gb, br, fr, es, be, nl, mx, kr, hu, pl (code pays fourni par la source)

GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo during the Second Part of the Third Observing Run

R. Abbott, T. D. Abbott, F. Acernese, K. Ackley et autres

The third Gravitational-Wave Transient Catalog contains 90 probable gravitational-wave candidates, including binary black holes, binary neutron stars, and black hole--neutron star binaries across a wide range of masses.

it, fr (code pays fourni par la source)

Simulation-based Inference with the Generalized Kullback-Leibler Divergence

B. Miller, Marco Orsini Federici, Christoph Weniger, Patrick Forré

In Simulation-based Inference, the goal is to solve the inverse problem when the likelihood is only known implicitly. Neural Posterior Estimation commonly fits a normalized density estimator as a surrogate model for the posterior. This formulation cannot easily fit unnormalized surrogates because …