Zobrazeno 1 - 10
of 381
pro vyhledávání: '"P. Kejzlar"'
We use two different methods, Monte Carlo sampling and variational inference (VI), to perform a Bayesian calibration of the effective-range parameters in ${}^3$He-${}^4$He elastic scattering. The parameters are calibrated to data from a recent set of
Externí odkaz:
http://arxiv.org/abs/2408.13250
One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar.
Externí odkaz:
http://arxiv.org/abs/2405.10839
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models.
Externí odkaz:
http://arxiv.org/abs/2311.01596
Autor:
Jiří Munzar
Publikováno v:
Brünner Beiträge zur Germanistik und Nordistik, Vol 26, Iss 1 (2013)
Externí odkaz:
https://doaj.org/article/c3de7a3108554c3ba5fde252f4863b8c
Autor:
Kejzlar, Vojtech, Hu, Jingchen
Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to thei
Externí odkaz:
http://arxiv.org/abs/2301.01251
Publikováno v:
Physical Review Research, Vol 6, Iss 3, p 033266 (2024)
One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar.
Externí odkaz:
https://doaj.org/article/dab828404090450fad1ce72090cca228
For many decades now, Bayesian Model Averaging (BMA) has been a popular framework to systematically account for model uncertainty that arises in situations when multiple competing models are available to describe the same or similar physical process.
Externí odkaz:
http://arxiv.org/abs/2106.12652
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect
Externí odkaz:
https://doaj.org/article/958a11b69a984d0d8064f3a42dc7c2f3
Publikováno v:
Stat Comput 31, 49 (2021)
Mathematical models implemented on a computer have become the driving force behind the acceleration of the cycle of scientific processes. This is because computer models are typically much faster and economical to run than physical experiments. In th
Externí odkaz:
http://arxiv.org/abs/2008.05021
Autor:
Kejzlar, Vojtech, Maiti, Tapabrata
With the advancements of computer architectures, the use of computational models proliferates to solve complex problems in many scientific applications such as nuclear physics and climate research. However, the potential of such models is often hinde
Externí odkaz:
http://arxiv.org/abs/2003.12890