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Interleaving bisection stimuli - randomly or in sequence - does not disrupt perceptual learning, it just makes it more difficult

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Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

Fabio Nobile, Lorenzo Tamellini

In this work we consider quasi-optimal versions of the Stochastic Galerkin method for solving linear elliptic PDEs with stochastic coefficients. In particular, we consider the case of a finite number

N

of random inputs and an analytic dependence of the s ...

2014

Interleaving bisection stimuli - randomly or in sequence - does not disrupt perceptual learning, it just makes it more difficult

Graph Chatbot

Chattez avec Graph Search

Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

Efficient inference for spatial extreme value processes associated to log-Gaussian random functions

Perceptual Learning, Roving, and Synaptic Drift

MATHICSE Technical Report : Convergence of quasi-optimal stochastic Galerkin methods for a class of PDES with random coefficients

Perceptual learning, roving and the unsupervised bias

Stochastic Control and Free Boundary Problems for Sailboat Trajectory Optimization

No association of the BDNF val66met polymorphism with implicit associative vocabulary and motor learning

Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

Perceptual Learning, Roving, and Synaptic Drift

MATHICSE Technical Report : Convergence of quasi-optimal stochastic Galerkin methods for a class of PDES with random coefficients

Perceptual learning, roving and the unsupervised bias

Stochastic Control and Free Boundary Problems for Sailboat Trajectory Optimization

No association of the BDNF val66met polymorphism with implicit associative vocabulary and motor learning

Efficient inference for spatial extreme value processes associated to log-Gaussian random functions