We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situ...
We propose certified reduced basis methods for the efficient and reliable evaluation of a general output that is implicitly connected to a given parameterized input through the ha...
Yanlai Chen, Jan S. Hesthaven, Yvon Maday, Jer&oac...
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors a...
Approximate policy iteration methods based on temporal differences are popular in practice, and have been tested extensively, dating to the early nineties, but the associated conve...
The Markov chain approximation method is an effective and widely used approach for computing optimal values and controls for stochastic systems. It was extended to nonlinear (and p...