Zobrazeno 1 - 10
of 5 674
pro vyhledávání: '"Posterior estimation"'
Computer simulations have long presented the exciting possibility of scientific insight into complex real-world processes. Despite the power of modern computing, however, it remains challenging to systematically perform inference under simulation mod
Externí odkaz:
http://arxiv.org/abs/2412.05590
Stochastic infectious disease models capture uncertainty in public health outcomes and have become increasingly popular in epidemiological practice. However, calibrating these models to observed data is challenging with existing methods for parameter
Externí odkaz:
http://arxiv.org/abs/2412.12967
This paper serves as a reference and introduction to using the R package dapper. dapper encodes a sampling framework which allows exact Markov chain Monte Carlo simulation of parameters and latent variables in a statistical model given privatized dat
Externí odkaz:
http://arxiv.org/abs/2412.14503
Neural posterior estimation (NPE), a simulation-based computational approach for Bayesian inference, has shown great success in situations where posteriors are intractable or likelihood functions are treated as "black boxes." Existing NPE methods typ
Externí odkaz:
http://arxiv.org/abs/2410.19105
Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of
Externí odkaz:
http://arxiv.org/abs/2410.16347
Autor:
Erickson, Sydney, Wagner-Carena, Sebastian, Marshall, Phil, Millon, Martin, Birrer, Simon, Roodman, Aaron, Schmidt, Thomas, Treu, Tommaso, Schuldt, Stefan, Shajib, Anowar, Venkatraman, Padma, Collaboration, The LSST Dark Energy Science
Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To enable time-delay cosmography from a sample of $\mathcal{O}(10^3)$ lense
Externí odkaz:
http://arxiv.org/abs/2410.10123
Autor:
Starostin, Vladimir, Dax, Maximilian, Gerlach, Alexander, Hinderhofer, Alexander, Tejero-Cantero, Álvaro, Schreiber, Frank
Reconstructing the structure of thin films and multilayers from measurements of scattered X-rays or neutrons is key to progress in physics, chemistry, and biology. However, finding all structures compatible with reflectometry data is computationally
Externí odkaz:
http://arxiv.org/abs/2407.18648
As the sensitivity of the international gravitational wave detector network increases, observing binary neutron star signals will become more common. Moreover, since these signals will be louder, the chances of detecting them before their mergers inc
Externí odkaz:
http://arxiv.org/abs/2407.10263
Autor:
Kolmus, Alex, Janquart, Justin, Baka, Tomasz, van Laarhoven, Twan, Broeck, Chris Van Den, Heskes, Tom
Modern simulation-based inference techniques use neural networks to solve inverse problems efficiently. One notable strategy is neural posterior estimation (NPE), wherein a neural network parameterizes a distribution to approximate the posterior. Thi
Externí odkaz:
http://arxiv.org/abs/2403.02443
Simulation based inference (SBI) methods enable the estimation of posterior distributions when the likelihood function is intractable, but where model simulation is feasible. Popular neural approaches to SBI are the neural posterior estimator (NPE) a
Externí odkaz:
http://arxiv.org/abs/2404.13557