Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Julian Kappl"'
Publikováno v:
Digital Transformation and Society, Vol 3, Iss 4, Pp 410-423 (2024)
Purpose – Due to the disruptive nature of digital transformation, firms can hardly ignore the further digitalisation of processes and business models. Implementing such initiatives triggers enormous investments in infrastructure and software, makin
Externí odkaz:
https://doaj.org/article/8cba2617d0574673957a747b0e8dc928
Autor:
Yuting I. Li, Günther Turk, Paul B. Rohrbach, Patrick Pietzonka, Julian Kappler, Rajesh Singh, Jakub Dolezal, Timothy Ekeh, Lukas Kikuchi, Joseph D. Peterson, Austen Bolitho, Hideki Kobayashi, Michael E. Cates, R. Adhikari, Robert L. Jack
Publikováno v:
Royal Society Open Science, Vol 8, Iss 8 (2021)
Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and the surveillance process through which data are acquired. We present a Bayesian inference methodology that quantifies these uncertainties, for ep
Externí odkaz:
https://doaj.org/article/b441258a482f4fada86339e1da709e71
Publikováno v:
Physical Review X, Vol 11, Iss 3, p 031022 (2021)
For diffusive stochastic dynamics, the probability to observe any individual trajectory is vanishingly small, making it unclear how to experimentally validate theoretical results for ratios of path probabilities. We provide the missing link between t
Externí odkaz:
https://doaj.org/article/86fd7e96230a41febb06a078f0f906fb
Autor:
Julian Kappler, Ronojoy Adhikari
Publikováno v:
Physical Review Research, Vol 2, Iss 2, p 023407 (2020)
The trajectories of diffusion processes are continuous but nondifferentiable, and each occurs with vanishing probability. This introduces a gap between theory, where path probabilities are used in many contexts, and experiment, where only events with
Externí odkaz:
https://doaj.org/article/f20c165dba1a42d7aa5efc31ac899904