Results 131 to 140 of about 249 (156)

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis. [PDF]

Appl Intell (Dordr)
Petkov H, MacLellan C, Dong F.
europepmc   +1 more source

Theories of Relativistic Dissipative Fluid Dynamics. [PDF]

Entropy (Basel)
Rocha GS, Wagner D, Denicol GS, Noronha J, Rischke DH.   +4 more
europepmc   +1 more source

Machine learning for estimation and control of quantum systems. [PDF]

Natl Sci Rev
Ma H, Qi B, Petersen IR, Wu RB, Rabitz H, Dong D.   +5 more
europepmc   +1 more source

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics. [PDF]

Front Neurosci
Areces-Gonzalez A, Paz-Linares D, Riaz U, Wang Y, Li M, Razzaq FA, Bosch-Bayard JF, Gonzalez-Moreira E, Lifespan Brain Chart Consortium (LBCC), Global Brain Consortium (GBC), Cuban Human Brain Mapping Project (CHBMP), Ontivero-Ortega M, Galan-Garcia L, Martínez-Montes E, Minati L, Valdes-Sosa MJ, Bringas-Vega ML, Valdes-Sosa PA.   +17 more
europepmc   +1 more source

Topological data analysis and topological deep learning beyond persistent homology: a review. [PDF]

Artif Intell Rev
Su Z, Liu X, Hamdan LB, Maroulas V, Wu J, Carlsson G, Wei GW, Wei GW.   +7 more
europepmc   +1 more source

Local limit theorems for functionals in the conditional invariance principle

Journal of Soviet Mathematics, 1988
Let \(\xi_ 1,...,\xi_ n\) be i.i.d. r.v.'s with E \(\xi\) \({}_ i=0\), E \(\xi\) \({}^ 2_ i=1\), \(i=1,...,n\), \[ X_ n(t)=n^{- }\sum^{[nt]}_{i=1}\xi_ i+n^{-}(nt-[nt])\xi_{[nt]+1},\quad t\in [0,1],\quad X_ n(0)=0, \] and \(X_ n^{(a,\epsilon)}\) be conditional processes defined by the following way: \[ P\{X_ n^{(a,\epsilon)}\in A\}=P\{X_ n\in A| \quad ...
openaire   +2 more sources

The Invariance Principle for Vector Valued Random Variables with Applications to Functional Random Limit Theorems

1981
1.Introduction. Let ξ1, ξ2,… be a sequence of independent real valued random variables with the same distribution with mean 0 and variance 1. Define the stochastic process ζn by $$\zeta _n \left( t \right) = S_{\left[ {nt} \right]} /n^{1/2} = \left( {\xi _1 + \cdots \xi _{nt} } \right)/n^{1/2} ,$$ where 0⩽t⩽1 and n = 1, 2,… The classical ...
T. Byczkowski, T. Inglot
openaire   +1 more source

Functional central limit theorems for persistent Betti numbers on cylindrical networks

Scandinavian Journal of Statistics, 2022
Johannes Krebs, Christian Hirsch
exaly  

Some Useful Functions for Functional Limit Theorems

Mathematics of Operations Research, 1980
Ward Whitt
exaly