Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Markos A. Katsoulakis"'
Publikováno v:
Journal of Statistical Software, Vol 85, Iss 1, Pp 1-28 (2018)
Stochastic simulation and modeling play an important role to elucidate the fundamental mechanisms in complex biochemical networks. The parametric sensitivity analysis of reaction networks becomes a powerful mathematical and computational tool, yieldi
Externí odkaz:
https://doaj.org/article/dea566ecd58b43628d4491b81a55de13
Autor:
Jinchao Feng, Joshua Lansford, Alexander Mironenko, Davood Babaei Pourkargar, Dionisios G. Vlachos, Markos A. Katsoulakis
Publikováno v:
AIP Advances, Vol 8, Iss 3, Pp 035021-035021-16 (2018)
We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adso
Externí odkaz:
https://doaj.org/article/2c9bb703ce914ed8b7e8a237ce015aaa
Autor:
Markos A. Katsoulakis, Gerhard R. Wittreich, Daniel J. Robinson, Geun Ho Gu, Dionisios G. Vlachos
Publikováno v:
The Journal of Physical Chemistry C. 125:18187-18196
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e614b6a0e27d1f6eba46fdc123194660
https://doi.org/10.1021/acs.jpcc.1c04754
https://doi.org/10.1021/acs.jpcc.1c04754
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 9:1457-1498
We present an information-based uncertainty quantification method for general Markov Random Fields. Markov Random Fields (MRF) are structured, probabilistic graphical models over undirected graphs, and provide a fundamental unifying modeling tool for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b09bf268b42c0b3ba79d3f3be463fc87
https://doi.org/10.1137/20m1374614
https://doi.org/10.1137/20m1374614
Publikováno v:
ESAIM: Mathematical Modelling and Numerical Analysis. 55:131-169
Quantifying the impact of parametric and model-form uncertainty on the predictions of stochastic models is a key challenge in many applications. Previous work has shown that the relative entropy rate is an effective tool for deriving path-space uncer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd1c8e3a93f33184380934e698d980c2
https://doi.org/10.1051/m2an/2020070
https://doi.org/10.1051/m2an/2020070
Publikováno v:
SIAM Journal on Mathematics of Data Science. 3:1093-1116
We derive a new variational formula for the Renyi family of divergences, $R_\alpha(Q\|P)$, between probability measures $Q$ and $P$. Our result generalizes the classical Donsker--Varadhan variation...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af7f177f90b5f4fc90125f826556fd32
https://doi.org/10.1137/20m1368926
https://doi.org/10.1137/20m1368926
Publikováno v:
IEEE transactions on neural networks and learning systems.
In this article, we propose a novel loss function for training generative adversarial networks (GANs) aiming toward deeper theoretical understanding as well as improved stability and performance for the underlying optimization problem. The new loss f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1653f708b81ad074f86f3799c615c620
https://pubmed.ncbi.nlm.nih.gov/35385390
https://pubmed.ncbi.nlm.nih.gov/35385390
Publikováno v:
PLoS ONE, Vol 10, Iss 7, p e0130825 (2015)
Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper,
Externí odkaz:
https://doaj.org/article/980103aedc1d4b5d9d27074c9be0e3c7
Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence. They allow us to integrate in a natural way expert knowledge, physical modeling, heterogeneous and correlated data and quan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::66f73f4fa9c94dfc91e43f5e8c72b51a
http://arxiv.org/abs/2107.08179
http://arxiv.org/abs/2107.08179
Publikováno v:
Science Advances
The developed framework apportions model error to inputs, computes predictive guarantees, and enables model correctability.
Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are o
Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a307e254f44a532e8abc60b9954002b
https://doi.org/10.1126/sciadv.abc3204
https://doi.org/10.1126/sciadv.abc3204