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DAGSTUHL
2004
13 years 6 months ago
Lower Bounds and Non-Uniform Time Discretization for Approximation of Stochastic Heat Equations
We study algorithms for approximation of the mild solution of stochastic heat equations on the spatial domain ]0, 1[ d . The error of an algorithm is defined in L2-sense. We derive...
Klaus Ritter, Thomas Müller-Gronbach
SIAMNUM
2010
114views more  SIAMNUM 2010»
12 years 11 months ago
A Posteriori Error Estimation Based on Potential and Flux Reconstruction for the Heat Equation
We derive a posteriori error estimates for the discretization of the heat equation in a unified and fully discrete setting comprising the discontinuous Galerkin, finite volume, mix...
Alexandre Ern, Martin Vohralík
MOC
2000
76views more  MOC 2000»
13 years 4 months ago
Optimal approximation of stochastic differential equations by adaptive step-size control
We study the pathwise (strong) approximation of scalar stochastic differential equations with respect to the global error in the L2-norm. For equations with additive noise we estab...
Norbert Hofmann, Thomas Müller-Gronbach, Klau...
JMLR
2010
140views more  JMLR 2010»
12 years 11 months ago
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact r...
Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
JMLR
2010
157views more  JMLR 2010»
12 years 11 months ago
Why are DBNs sparse?
Real stochastic processes operating in continuous time can be modeled by sets of stochastic differential equations. On the other hand, several popular model families, including hi...
Shaunak Chatterjee, Stuart Russell