134 search results - page 3 / 27 » The empirical behavior of sampling methods for stochastic pr... |
ICANN
2010
Springer
13 years 8 months ago
2010
Springer
Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorent...
APPROX
2005
Springer
14 years 29 days ago
2005
Springer
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
CCE
2004
13 years 7 months ago
2004
This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-st...
BMCBI
2006
13 years 7 months ago
2006
Background: Character mapping on phylogenies has played an important, if not critical role, in our understanding of molecular, morphological, and behavioral evolution. Until very ...