Importance sampling in stochastic programming : a Markov chain Monte Carlo approach
Year of publication: |
2015
|
---|---|
Authors: | Parpas, Panos ; Ustun, Berk ; Webster, Mort ; Tran, Quang Kha |
Published in: |
INFORMS journal on computing : JOC. - Catonsville, MD : INFORMS, ISSN 1091-9856, ZDB-ID 1316077-1. - Vol. 27.2015, 2, p. 358-377
|
Subject: | Benders’ decomposition | cutting plane algorithms | stochastic optimization | stochastic programming | importance sampling | variance reduction | Monte Carlo | Markov chain Monte Carlo | kernel density estimation | nonparametric | Monte-Carlo-Simulation | Monte Carlo simulation | Theorie | Theory | Markov-Kette | Markov chain | Stochastischer Prozess | Stochastic process | Mathematische Optimierung | Mathematical programming | Stichprobenerhebung | Sampling | Algorithmus | Algorithm |
-
Joint Bayesian analysis of parameters and states in nonlinear, non-Gaussian state space models
Barra, István, (2014)
-
A sample average approximation method for disassemblx line balancing problem under uncertainty
Bentaha, Mohand Lounes, (2014)
-
Ardia, David, (2009)
- More ...
-
A stochastic multiscale model for electricity generation capacity expansion
Parpas, Panos, (2014)
-
Webster, Mort, (2012)
-
The curious role of "learning" in climate policy : should we wait for more data?
Webster, Mort, (2002)
- More ...